text:
|Publication number
|Publication type
|Application number
|Publication date
|Aug 20, 1996
|Filing date
|Mar 14, 1994
|Priority date
|Mar 14, 1994
|Fee status
|Also published as
|Publication number
|Michael J. Martino, Robert C. Paulsen, Jr.
|Original Assignee
|International Business Machines Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (9), Referenced by (155), Classifications (8), Legal Events (5)
|External Links: USPTO, USPTO Assignment, Espacenet
The subject invention relates generally to human language recognition technology, and particularly to a machine process for identifying the language used in a computerized document.
Computer networks have proliferated the transmission of coded documents between people with different languages. When electronic mail is sent across national boundaries, computer system operations may need to be interrupted for human intervention to determine the language of a received document before its machine processing (selecting, displaying, printing, etc.) can continue. This invention eliminates the need for such human intervention by automatically determining the correct language alphabet in which to print, display, etc. the document when using computer recorded codes representing its text to allow the machine processes to continue without interruption through the language identification process.
Prior art in the area of automated language identification of coded text is limited to n-gram character based systems (e.g. U.S. Pat. Nos. 4,829,580 to Church and 5,062,143 to Schmitt), which handle each character multiple times, as opposed to the word-based technique used by the subject invention in which each character in a coded text is, at most, handled only once and most characters in large documents need not be handled at all.
Language identification cannot be done by prior signal recognition systems (e g U.S. Pat. No. 4,610,025 to Blum and PCT application WO 91/02347), which use language and speech characteristics that require large amounts of text to be parsed and measured, and large amounts of time for processing.
Prior computer-based language identification systems for identifying the language of a coded document are based on some form of matching algorithm based on language statistics that are related to or derived from the language, and typically use an algorithm which is not meaningful in a linguistic context. Attention may be directed to the following U.S. Patents for illustration of such prior systems: Schmitt, "Trigram-Based Method of Language Identification" U.S. Pat. No. 5,062,143 and Church, "Text Analysis System With Letter Sequence Recognition and Speech Stress Assignment Arrangement" U.S. Pat. No. 4,829,580.
Prior systems using trigrams, n-grams, and other artificial divisions in a computerized text are not considered reliable, and they are very slow and costly of computer time, because they handle each character multiple times for a document, e.g. each document character appears in three different trigrams. Characteristics measured, or derived from, but which are not actual components of written languages (such as trigrams or letter sequences), have limited success in identifying the correct language, and require large amounts of text to be parsed and measured, and large amounts of time for this processing.
Similarly, prior systems which depend on the attributes of individual characters and their local contexts are also limited when applied to the problem of identifying a language. For example, U.S. Pat. No. 4,058,795 to Balm entitled "Method and Apparatus for Context-Aided Recognition" may be useful in determining a word in a known language, but it is not suited to the problem of identifying the language in which the word is written.
Thus, the subject invention does not use the prior art techniques which variously classify language by examining selected characteristics of a signal waveform or language trigrams, n-grams, or other such artificial divisions of written language. Other attempts at system identification of languages have been based on various characteristics of speech signals are not useful since code-represented text does not have speech signals. Even for signals derived from the human voice, most languages share a significant number of sounds, making speech signals difficult to use for language identification. Added to this difficulty is the waveform processing requirement which tends to limit such approaches due to cost and complexity.
The subject invention provides a process for a machine determination of the language or genre used in a computer coded document.
In this specification, the term "language" means a natural language (i.e. human language) used for human communications (e.g. English, French, Spanish, German, etc.). The invention also applies to "genre" within a natural language. Genre is defined herein as a distinctive style of use of a language in some specific context. For example, genre within English includes technical writing, business writing, legal writing, fiction, and many others. Thus, genre applies to different variations of the same language involving different styles and manners of word use within a natural language which are reflected in coded documents, and may involve a distinctive use of standard words in a language or may add new words to a language. Genre may reflect particular jargons and dialects of a language such as High German, Low German, and Swiss German, or as London English and New York English.
The technique used by this invention reads word codes from a document and compares these word codes to predetermined lists of words selected from a plurality of languages or genre of interest. The language or genre of the document is identified by a process that determines which language's word-list most closely matches the words in the document. Closeness of match is weighted by the frequency of occurrence of listed words in each language or genre of interest.
Each language's or genre's word-list with the associated frequency of occurrence for each word in the list is referred to herein as a Word Frequency Table (WFT).
Each Word Frequency Table contains words from one language or genre and preferably contains relatively few words. The most common words in a language/genre are used in each table since, statistically, a significant percentage of all words in any document will be the most common words used in its language. That this is so is intuitive. Each of the words in each table has an associated statistically-determined value for the word's frequency of occurrence in the language or genre associated with the table.
The language/genre detection process associates each word frequency table with a respective accumulator. Initially, all accumulators are set to zero before recognition processing starts for identifying the language in a coded document.
The process reads a sequence of words from an inputted document having its language/genre identified. Each read word is compared to the common words in all tables. Whenever a read word is found to be equal to a word in any table, the process adds the word's Normalized Frequency Occurrence (NFO, as found in that table) to the current sum in the accumulator associated with the respective language. In this manner, values in the accumulators build up into language discriminating values during processing of the document's coded text characters.
When processing stops, the identified language/genre is the language/genre associated with the highest-valued accumulator. Processing may stop either by reaching the end of the document or by achieving a predetermined confidence in the accumulated discrimination. This is more fully described below.
As described, the mechanism can be seen to apply for any language or genre using an alphabet without regard to the printed symbols used for that language since all such symbols are processed as binary codes in a computer machine, and not as human readable symbols.
Ideographic languages such as Japanese, Chinese, Korean, and the Kangi languages for example, can be handled by this invention in a computer mechanism by choosing an appropriate internally-coded representation for the ideographs, such as the double-byte codes used for such languages in computers today. Thus, the mechanism of this invention can be applied to any language having coded words, without regard to the alphabet or word form required for the human-readable printed symbols used for the respective languages. This allows the invention to be used to recognize both alphabetic and ideographic languages by choosing an appropriate internally-coded representation for the words and ideographs.
The speed of language determination by this invention is very fast, because only a relatively small number of words need to be read from any document to reliably determine its language or genre.
Further, a unique advantage of the present invention is that only a few words (such as 50) need by contained in the Word Frequency Table for each language of interest, so that in practice each read word needs to be compared with only a relatively small number of words to suffice for reliable language recognition.
The use of NFOs by this invention aids in discriminating among words which may appear in more than one language. Use of NFOs increases the language detection ability of the mechanism, because of their normalization of the frequency of occurrence for the words in each table. The different NFO values for the same coded word in different languages (summed in the respective accumulators) differently affect the language-discrimination sums being accumulated. For example, "que" is a common word in both French and Spanish, but it has a larger NFO in the Spanish language. So counting the occurrences of "que" in a document will sum greater in the Spanish accumulator than in the French accumulator.
It should therefore be apparent that these different NFO values for "que" in the Spanish and French language sets are more effective than merely using a process that increments by one the current values in the Spanish and French accumulators each time the word "que" is read.
Use of NFOs (instead of simple occurrence counts) is especially important when this invention is used to distinguish between different genre of the same language. For example, one may wish to determine if a document is a "business" versus "technical" document. Unique word tables would be required for each genre and would-likely contain many the same words but differ in the NFO values for those words.
The frequency of occurrence of words in different languages can be obtained from a number of different publications. For example, see the FREQUENCY DICTIONARY OF FRENCH WORDS, authored by A. Juilland, D. Brodin and C. Davidovitch, Mouton, The Hague, Mouton, 1970, in the series, The Romance Languages and Their Structures. The most frequently occurring word in French is "le" and its measured frequency is 38,585 (page 387). Que, according to the same source has, in French, a measured frequency of 9,570 (page 387).
Similarly, in the FREQUENCY DICTIONARY OF SPANISH WORDS, in the same series of books, The Romance Languages and Their Structures, the most-frequently occurring word in Spanish is "de" and its measured frequency is 35,144 (page 385). Que, according to the Spanish frequency dictionary has a measured frequency of occurrence of 7,518 (page 385).
Other language data are found in I DANSKE AVISER, Ugeblade og Fagblade, av B. Maegaard og Hanne Ruus, Gyldendal, Copenhagen, 1986 (Danish), WORDFREQUENTIES IN GESCHREVEN EN GESPROKEN NEDERLANDS, P. C. Uit den Boogaart, Oosthoek, Scheltema & Holkema, Utrecht, 1975, (Dutch) A WORD-FREQUENCY DICTIONARY OF POLISH JOURNALISTIC TEXTS, F. E. Knowles, Dept. of Modern Languages, University of Aston, Birmingham, U.K., 1981 (Monograph), FREQUENCY DICTIONARY OF CHINESE WORDS, E. S. Liu, Mouton, The Hague, 1973, and EIN FREQUENZWOERTERBUCH DER DEUTSCHEN ZEITUNGSSPRACHE, I. Rosengren, CWK Gleerup Lund, Schweden. (Swedish) And this list is not exhaustive, but rather shows the breadth of languages for which the required frequency counts are readily available.
The data for Table 1 are taken from Computational Analysis of Present-day American English by H. Ducera and W. N. Francis and published by the Brown University Press, Providence, R.I., 1967. Similar books have been published for essentially all major languages. Some of these books for the major languages of the world include, in addition to the ones listed earlier, FREQUENCY DICTIONARY OF ITALIAN WORDS, authored by A. Juilland, et al, The Hague, Mouton 1973, in the series, The Romance Languages and Their Structures, HYPPIGE ORD.
An example is the NFO for "le" of 100=((100*(38585/38585)) in French, and the NFO for "que" is 24.8=(100*(9570/38585)) in French.
In Spanish, the NFO for "de" is 100=(100*(35144/35144)), and the NFO for "que" is 21.4=(100*(7518/35144)).
From these calculations of the NFO for the single word "que", it can be seen that, in examining computerized documents respectively written in French and Spanish, the probability of a document containing "que" in French is 24.8/(21.4+24.8)=0.537, and the probability of a document containing "que" in Spanish is 21.4/(21.4+24.8)=0.463. But the language-identification probability greatly increases for a language when a significant number of common words are examined in a document, and all of their NFOs are accumulated for the respective languages. Then, the language associated with the accumulator having the largest accumulated sum is the identified language for the document.
Therefore, if all the words read from a document provide the highest value in the French accumulator, the probability that the document is written in French is so great that it can be reliably used.
Table 1 below demonstrates why relatively few words are required to be in a Word Frequency Table. It lists the twelve most common words in written English in MEASURED FREQUENCY order from top to bottom. The MEASURED FREQUENCY is stated as the average count for each common word per 100 words used in the (written) English language, determined over the total word count in a very large number of English documents. It is therefore the percentage of cases that a word randomly selected from a representative sample of written English text will be that particular word. (MEASURED FREQUENCY is equivalent to frequency of occurrence, as that term has been previously used herein.) Each CUMULATIVE FREQUENCY in Table 1 is the sum of the MEASURED FREQUENCIES for all words in the table from that point and above. For example, the CUMULATIVE FREQUENCY of 20.307 is shown with the word "in" in TABLE 1 and represents a plurality of common words comprising the six most frequently used words in the English language (i.e. the first six words: "the", "of", "and", "to", "a", and "in" in Table 1). The CUMULATIVE FREQUENCY of 20.307 is therefore derived by adding the MEASURED FREQUENCIES for these six words. Thus, in a representative sample of written English text, approximately 20% of the words in the text will be one of the first six words in Table 1; fully 26% will be one of the 12 words in the table. Thus, relatively few words represent a substantial portion of representative text. Similar tables showing similar statistics can be constructed for other languages.
TABLE 1______________________________________The Twelve Most Common English Words MEASURED CUMULATIVEWORD FREQUENCY FREQUENCY______________________________________the 6.899 6.899of 3.590 10.489and 2.845 13.334to 2.578 15.912a 2.291 18.203in 2.104 20.307that 1.045 21.352was 0.995 22.347he 0.968 23.315for 0.941 24.256it 0.936 25.192with 0.863 26.055______________________________________
Experiments have shown that after about 100 words are read from a document being examined to determine its human language, the identification of the correct language is essentially assured by the highest sum of NFOs in the accumulators for the candidate languages. It is preferable that several hundred words be read from a document (unless it is a very short document) to conclude that the language identification is assured with a high degree of confidence. For short documents, the language assurance is less, but generally is sufficient.
Language statistics, such as those shown above in Table 1, show that the words which are most common in a language are the most useful in making a language identification. There are several reasons for this: First, the definition of a common word in a language is that it is a word that is used frequently in that language. As a result, common words from a language will appear in essentially all written communication; hence the process of this invention will find them. (As an aside, cryptographers have known for a long time that common words can provide an opponent with an entry into a code or cipher. In certain situation then, messages are encoded or enciphered avoiding the common words. This fact provides an independent confirmation of the usefulness of common words in the determination of the language of a document.)
It can be argued that uncommon words are actually a better discriminator among languages and while true, it is not a particularity useful observation. For example, "undoubtedly" occurs only (so far as is known to the inventors) in English, and hence may be taken as an absolute indication that a document containing it is written in English. However, the word "undoubtedly" occurs so infrequently, as to be of no value in discriminating among languages. Using large tables of uncommon words will increase discrimination (either alone or in combination with common words). Uncommon word tables for languages of interest are intended to be included in the concept of this invention, so that if all the words in a given document are found in a table of uncommon words of one language, L, it is assured that the document is written in language L.
While a number of words are common in different languages, such as "que", in Spanish and French and "in" which is common in Dutch, English, German and Italian, when the normalized frequencies of occurrence are computed for the different languages, the values differ. That this is so can be seen from Table 2, and can be understood from noting that the word "in", while spelled the same in the standard alphabet, has different meanings and may have differences among the languages, in meaning, part of speech, usage and so on. "Que", for example, in French is most commonly used as a conjunction whereas in Spanish, the most common usage is as a pronoun.
TABLE 2______________________________________Normalized Frequency of the Word "IN"for Several LanguagesLANGUAGE NORMALIZED FREQUENCY OF "IN"______________________________________Dutch 35.54German 47.55English 30.50Italian 39.87______________________________________
The overall effect of these language statistics is that an examination of about 100 words form a document in an unknown language using the method of the present invention with the 50 most common words in each of the candidate languages and their normalized frequencies of occurrence is ordinarily sufficient to estimate the language of the document accurately.
In fact, in many instances, fewer than 100 words need to be examined and still smaller word sets can be used. For example, 25 most common words with their NFO's will often yield a satisfactory result. Some difficulty may be experienced with very short messages. However this is no different from what a skilled linguist is confronted with when asked in which language the message, "Die." written. Lacking any contextual clues or other words, the best guess is German as die has a very high frequency of occurrence in German, but Dutch and English are also candidate languages.
FIG. 1 is a functional block diagram of the current invention, a general data processing system containing data and controls for determining the language of a document.
FIG. 2 is a flow chart of sequence of general steps carried out in the process performed by the system shown in FIG. 1. Detailed steps within each of these general steps are shown in FIGS. 5, 6, 7 and 8.
FIG. 3 is an example of Word Frequency Tables (WFTs) respectively containing a set of common words with associated fields containing Normalized Frequencies of Occurrence (NFOs) for a plurality of languages of interest.
FIG. 4 is an example of Word Frequency Accumulators (WFAs) respectively associated with the WFTs in FIG. 3.
FIG. 5 is a flow chart of detailed initialization steps executed within step 202 in FIG. 2.
FIG. 6 is a flow chart of detailed steps executed within step 203 of FIG. 2 in a next word sub-process for locating the words in a document having its language being determined.
FIG. 7 is a flow chart of detailed steps executed within step 204 of FIG. 2 in a comparator sub-process for identifying common words in a document and reading their NFOs into their associated WFAs of the languages of interest.
FIG. 8 is a flow chart of detailed steps within step 206 of FIG. 2 for a language selection sub-process which identifies the human language of a source document based on the highest sum of the NFOs in any WFA.
The preferred embodiment of the present invention is the combination of a novel programming executing in a conventional data processing system. Alternatively, the invention may be embodied in special data processing hardware (not shown) which has separate comparator controls for operating with tables embedded in special storage hardware.
FIG. 1 shows an overall block diagram of a general system configuration supporting an embodiment of the invention. It contains components which may be formed of hardware/software components in the system.
Document Source 101
Coded data input is provided from a Document Source 101, which provides coded characters representing text of a document generated by using words of any human language. Document source 101 may be provided by an optical character reader reading the coded characters from a hard copy of a document. Alternatively, source 101 may be coded characters read from a soft copy of a document stored in an internal storage (e.g. RAM, floppy disk, tape or compact disk) of the system, or source 101 may be coded characters transmitted on a network to the data processing system from some remote location using a similar system reading the document from one of its terminals or peripheral devices.
Processor 105 and Word Comparator 103
The text from the source 101 is provided in variable-length word units to processor comparator controls 103, which are part of a processor 105. Comparator controls 103 compare each variable length word received from source 101 with each word in a plurality of language Word Frequency Tables 102.
Word Frequency Tables (WFT) 102.
Each of the Word Frequency Tables 102 is associated with a respective language which can be detected as the language of source document 101. The set of particular languages represented by tables 102 are called herein the languages of interest, and a language not represented in tables 102 cannot be detected as the language of document source 101.
The Word Frequency Table for a language or genre of interest may be generated by the following process:
1. Counting the occurrence of each word in a large number of text documents for a language or genre to find the most commonly-used words which are selected and written in a list with their associated occurrence count;
2. Determining a frequency of occurrence for each word in the list by dividing the word's occurrence count by the total number of words examined in all documents by step 1 and replacing the occurrence count with the calculated frequency of occurrence;
3. Reducing the size of each word-list by selecting the N number of words in the list having the highest frequency of occurrence count (in practice 25 to 100 words in a list is sufficient for language discrimination, with more required for genre discrimination);
4. Normalizing the frequency of occurrence for each word in the word-list by dividing the word's frequency of occurrence by the highest frequency of occurrence (designating the most-used word), or by dividing by the sum of all frequency of occurrence counts, in the respective word-list. Either of these methods obtains normalized-frequency of occurrence (NFO) values. The NFOs may be further adjusted by multiplying all NFO values by a common factor to achieve a more convenient scale (for example making the most-frequent word's NFO value be 100);
5. Using the selected words (step 3) and the associated NFO for each word (step 4) to comprise the Word Frequency Table for the language or genre.
Thus, each of the Word Frequency Tables 102 contains a set of the most frequently used words in a specific language or genre and values for the respective frequency of occurrence of each word in the language associated with that frequency table.
Thus, in the example of FIG. 3, the most common word in each of the languages of interest has its NFO value set to 100 for each language, and the NFOs of the other common words in each table is less than 100.
The use of NFOs, instead of "measured frequencies" in the WFTs significantly improves the language identification ability of the process, for the reasons given in the Summary of Invention section in this specification.
The word frequencies for the words in each language or genre of interest may alternatively be determined (instead of using the source documents previously listed herein) by having a data processing system count the words in a large number of documents in an environment in which the invention is to be used. This approach may be more useful than using the listed sources when the particular environment mixes jargon, or colloquialisms, in one or more of the languages of interest, which often occurs, or if one is attempting to distinguish different genre in the same language.
As a variation on the basic invention, any of Word Frequency Tables 102 may also contain a sampling of words unique to the same language or genre which may have a relatively low frequency of use in the language of the respective table. Each of the common words in tables 102 has an associated frequency value, as described above. However, each of the unique words in the tables 102 has an artificial frequency value that is large in comparison to the frequency values of the common words. For example, a value twice that of the most common word may be used. Use of unique words is especially helpful in distinguishing between genre.
Each word received from document source 101 is compared by comparator 103 with the words in tables 102. Comparator 103 determines an equal or unequal condition between each inputted document source word and each table word read from tables 102. The words in each of the plurality of tables 102 may be scanned until all words in all of tables 102 are compared by comparator 103.
Whenever a word in any Word Frequency Table 102 is found to compare equal with a source document word, the frequency value associated with the table word is read out by processor 105. Then, that frequency value is transferred by processor 105 to a language-associated accumulator (of a plurality of accumulators 104) which is the accumulator associated with the language of the table having the current word with the compare-equal condition and called a Word Frequency Accumulator (WFA). Each frequency value received by an associated Word Frequency Accumulator 104 is added to (accumulated with) whatever sum currently exists in the respective accumulator.
The compare processing stops when the source words stop being inputted. This may occur when the document source 101 reaches the end of the current document or reaches a predetermined number of words, or when (after some minimum number of words are supplied by the document source 101) one the highest-valued accumulator 104 exceeds the next highest-valued accumulator 104 by a predetermined amount or factor. See discussion of step 205, below, for details of this.
After the compare processing stops for a document source 101, language-selector controls 106 are operated by the processor 105. The language-selector controls 106 access all accumulated values in all accumulators 105 and determines which accumulator contains the highest value.
The language associated with the accumulator containing the highest value is outputted by controls 106 as the identified language.
Word Frequency Accumulators 104
A plurality of Word Frequency Accumulators (WFAs) 105 are respectively associated with each language's WFT. That is, each language has an associated WFT and an associated WFA.
Thus, a WFT/WFA pair is thereby provided for each language of interest. The associated language is recorded with each WFA and each WFT. Examples of WFAs are represented in FIG. 4, which are respectively associated with the WFTs in FIG. 3. All of the WFAs are reset to a zero value by initialization step 402 prior to inputting words from a document source 101 to the process in FIG. 1 or 2.
During processing, each WFA contains a running total of the NFOs of the common words in the source document which match (compare-equal with) a common word in the associated WFT (for the same language). For each match of a word in the source document with a word in any of the WFT, the normalized frequency of occurrence for that word is added to the total for that language in the associated WFA.
Hence, each WFA sums the NFOs for inputted words having matches in the associated WFT. A match occurs when a source word from an inputted document is found equal to a word in the associated WFT, causing the NFO of that word to be sent to, and summed in, its associated WFA. After reading is ended for an inputted document, the language assigned to the WFA having the highest value of all WFAs is thereby declared by the process as the identified language of the document.
Process in FIG. 2
The process in the flow chart of FIG. 2 shows the general sequence of steps by which a document is processed and its language determined. The process is described here without reference to the use of unique words (the variation discussed above) because the process is the same in either case, the difference being confined to the construction of the Word Frequency Tables as described above.
Each of steps 202, 203, 204 and 206 represents a sub-process, which is expanded into a sequence of sub-steps respectively represented in FIGS. 5, 6, 7 and 8. Each of these steps operates under control of a processor in the data processing system.
General step 202 provides initialization of a data processing system for executing the invention using WFTs and WFAs for the languages of interest. Step 202 loads the WFTs and WFAs into the system main memory from a storage medium, on which they were stored after they were previously prepared, and their addresses in main memory is passed to the process in FIG. 2 for use in its execution. Step 203 obtains the first word and a sequence of following words from the source document which is having its language determined and transfers each word to step 204. The first word need not be the actual first word in the document, and it may be located anywhere in the source document, and it is generally the first text word after any non-text data in the source document. Non-text data will generally be skipped in the sequence of data provided to the process in FIG. 2.
Step 204 compares each source word with all of the common words in all WFTs and reads out the NFOs associated with each common word getting a match (compare-equal condition). Step 204 adds each readout NFO to the current sum in the WFA associated with the WFT having the readout.
Step 205 determines when to stop processing a document. This may be when any of the following conditions are detected:
1. The entire document is read and all words compared to the Word Frequency Tables. This may be determined by recognizing an end-of-document signal, by knowing the size of the document and tracking the amount of text processed, by sensing that a time has expired during which no input has been received as an indication of the end of a document, or by relying on the data processing system's file-processing subsystem to report end-of-file.
2. A predetermined number of words from the document have been processed. It has been found that only a hundred words or so may be needed from a document to identify its language. Thus, all words in a large document need not be read and processed by the subject invention. Hence, the invention may use a predetermined number N for the number of words required to be read for language or genre needing the most number of words for identification.
3. After some minimum number of words are processed, a predetermined factor (or, alternatively, difference) between the highest-valued and the next-highest-valued accumulator is achieved. It has been found that once the accumulators diverge it is extremely unlikely that additional processing will result in the then-highest-valued accumulator being exceeded by another accumulator when the entire document is processed.
Step 206 is entered after the end of document is sensed by step 205. Step 206 compares the values in all of the WFAs and finds the WFA having the largest value. Step 206 outputs the language-name content of the language field associated with the WFA having the largest value to determine the identified language.
Detailed Steps in FIG. 5 for Process 202
FIG. 5 shows steps for the initialization process 202 in FIG. 2, which is entered when the process in FIG. 2 is started. Thus, in FIG. 5, step 501 is entered from step 201 in FIG. 2. Step 502 sets up and loads into the main storage of the data processing system the WFT and WFA for each language of interest to be used in the execution by the process of FIG. 2. Preferably, the WFTs and WFAs were previously prepared and stored on a disk device in the manner discussed above in this specification. Hence, step 502 loads the WFTs and WFAs from the disk device into a main storage of a data processing system and sets up their main storage addresses within software for the process in FIG. 2, in the manner done by conventional software initialization processes.
After step 502 is completed, step 503 sets the contents in all WFA accumulators to a zero state. Then, step 504 accesses the input source document and locates and reads its first word. Each word in a coded document is delineated by conventional word delimiters. Such delimiters are primarily "blank" characters, but also include the comma (,), semicolon (;), period (.), question mark (?), and other punctuation marks in each of the languages of interest.
The initialization operation in FIG. 6 may then end at step 505 with a return to the main-line process in FIG. 2, in which step 203 is entered.
Detailed Steps in FIG. 6 for Process 203 of FIG. 2
Step 203 in FIG. 2 is executed by entering step 601 in FIG. 6. Then step 602 is invoked to scan the characters received from the document in order to delineate its words, which are needed by the process of this invention. Each next source word is determined by detecting a next word delimiter (which initially is just prior to the beginning of first word in the source document), which is detected by step 603.
As the characters are received, they are stored into a word buffer W and the storing of characters stops momentarily when the next word delimiter is detected, to define the next word in buffer W; this word is then referred to herein as the "current word". The process in FIG. 6 defines one current word at a time from the source document, and then step 605 returns to the main-line process in FIG. 2 by entering step 204.
The effect of this delimiter processing is to parse the source document into its words and present each source word to the comparator process 204 in FIG. 2 (which is shown in more detail in FIG. 6).
Detailed Steps in FIG. 7 for Process 204 of FIG. 2
The process in FIG. 7 receives each current word from buffer W and compares it with each common word in all WFTs, beginning with a first selected WFT in the set of WFTs (which represent all of the languages of interest).
Step 702 starts by selecting the first WFT in the set which can be any of the WFTs in the set. Then the WFTs can be selected in any order until all are compared to the received source word. Thus, step 702 may select, for example in FIG. 3, the Danish WFT as the first WFT.
Step 703 then compares the current word with the common words in the WFT to determine if the current word is present in that language's table. If the current word is not in that table, the no exit is taken from step 703 to step 705 which indicates if the current WFT is the last WFT to be examined in the set. If this WFT is not the last in the set of WFTs, the no exit is taken from step 705 back to step 702, which then selects the next WFT in the set for a next iteration through the sub-process in FIG. 7.
If step 703 finds a compare-equal condition, then the yes exit is taken to step 704 which accesses the NFO with the WFT word having the hit and adds that NFO to the current sum in the associated WFA for that language. Then, step 705 is entered to test if the current WFT is the last. If not the last, the no exit is taken to step 702, and another iteration through the process in FIG. 7 is taken until all WFTs are compared to the same source word.
The effect of the process in FIG. 7 is to generate for all of the languages of interest, running NFO totals in the WFAs for hit source words.
When the last WFT in the set is compared, step 705 takes its yes exit to step 706 which returns the process operations to step 205 in the main-line process in FIG. 2 to determine if the current source word is the last word to be examined in the source document.
Thus, the current source word may not be found in any WFT, or it may be found by step 703 to be in one or more WFTs for one or more of the languages of interest, whereby its NFO may be added to the contents of one or more WFAs.
Detailed Steps in FIG. 8 for Process 206 of FIG. 2
Step 206 selects the language associated with the WFA having the largest total, which is thereby identified as the language of the source document. The processing for step 206 begins by entering step 801, which goes to step 802 to measure all of the values in the WFAs to determine the largest value. When step 802 determines the largest value in all of the WFAs, step 803 is executed to access the language-name field associated with the WFA containing the highest value, and the language name content of that field is outputted to indicate the identified language for the current source document. The process in FIG. 8 is then complete, and its next step is taken to return to the main-line process in FIG. 2, which then ends with its last step 208.
If a next source document exist, the process in FIG. 2 is repeated for determining its language.
While particular embodiment have been shown and described, it is to be understood that this invention is not intended to be limited to this description but is susceptible to numerous changes and modifications as would be known to one skilled in the art and therefore there is no intention to be limited to the details shown herein but rather the intent is to cover all such changes and modifications as are obvious to one skilled in the art.
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|U.S. Classification
|704/1, 704/9, 704/2
|International Classification
|Cooperative Classification
|European Classification
|Mar 14, 1994
Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MARTINO, MICHAEL J.;PAULSEN, ROBERT C.;REEL/FRAME:006939/0953
Effective date: 19940311
|Dec 29, 1999
|Fee payment
Year of fee payment: 4
|Mar 10, 2004
|Maintenance fee reminder mailed
|Aug 20, 2004
|Lapse for failure to pay maintenance fees
|Oct 19, 2004
|Expired due to failure to pay maintenance fee
Effective date: 20040820


text:
Pulled into the parking lot and this hotel looked like it was shuttered. The sign on the building was covered with a white tarp and the lawn hadn't been trimmed in a while. The appearance was so abandoned that we called to check the hotel was indeed operational. A nice young woman answered and was accommodating enough to cancel the reservation so that we could find alternative lodging. I can't say how the actual hotel appeared inside, but the outside was enough to send us away quickly.
- Official Description (provided by the hotel):
- The Clarion Hotel is conveniently located for both business and leisure travelers on the north side of Muscatine, IA just three miles from the Mississippi riverfront. The Iowa hotel also is a short drive to Bridgestone Bandag, AllSteel, HNI and the Muscatine Soccer Complex. Other attractions in the Muscatine area include Wild Cat Den State Park, Muscatine Art Center (includes Musser Mansion and the Stanley Gallery), Weed Mansion, and Alexander G. Clark House. Two historic districts listed on the National Register of Historic Places (Downtown and West Hill). Guests of this Muscatine hotel can enjoy the free continental breakfast, indoor heated pool and whirlpool along with the 24-hour fitness center. All rooms feature coffee makers, microwave, refrigerator, irons and ironing boards, well lit desk areas and premium TV channels. Business travelers will welcome additional conveniences featuring the 24-hour business center with fax and copy service. The next time your travels bring you to Muscatine, IA, the Clarion Hotel would be delighted to accommodate your lodging needs. We look forward to seeing you soon! ... more less
- Reservation Options:
- TripAdvisor is proud to partner with Hotels.com and TripOnline SA so you can book your Hotel Muscatine Iowa reservations with confidence. We help millions of travellers each month to find the perfect hotel for both holiday and business trips, always with the best discounts and special offers.
- Also Known As:
- Holiday Inn Muscatine
- Muscatine Holiday Inn

subset: commoncrawl


text:
"The success of failure: Pulitzer winner's surprising road to the top" discusses not only the writing process, but also more generally the importance of failure and grit for creativity. Jennifer Egan, the Pulitzer winning author of A Visit from the Goon Squad, sometimes writes up to 50 or 60 drafts of her novels. While she calls her first drafts "terrible," she tirelessly works to improve upon them by editing, outlining, and discussions with other writers; and then by repeating each step as many times as necessary. "The key is struggling a lot," Egan says. Egan allows herself to engage in the creative struggle, which requires her to "fail" several times in her writing process before she finds her way to the often-successful end product. In writing and other creative endeavors, failure can actually pave the way to creativity. One just needs to allow themselves to learn from mistakes rather than be paralyzed by them. Read the full text here:
Wednesday, February 15, 2012
The importance of failure in the creative process
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text:
The Werewolf Game #2923 : Werewolf Game< Back to the game list
This game is moderated by talaisawsome. Please read the rules and contact the moderator with any questions or concerns about this game.Voting Discussion
This game finished in 6 rounds. The Werewolves Won!
Day4wheels voted for sammydj
buu441 voted for faheem1
Coolnine9 voted for sammydj
Flash1201 voted for world_peace_73
sader2011 voted for sammydj
Summer-Ktreva voted for sammydj
world_peace_73 voted for Coolnine9
At dusk, sammydj was shot with the silver bullet.
The Seer is divining the identity of Coolnine9
NightCoolnine9 voted for world_peace_73, 4wheels, sader2011
Flash1201 voted for world_peace_73, buu441, 4wheels
world_peace_73 was eaten before dawn.


text:
6x6 Multi-Channel Sound Card for Desktop with Cable w/ AES/EBU or S/PDIF
Product Code 31313
Retail Price
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The VX442 features six inputs/outputs with exceptional audio quality at 96kHz/24-bit. It's all you need for professional multi-channel applications such as audio production or archiving.
Digigram's versatile VX442 features four balanced analog inputs/outputs with 24-bit converters, as well as a digital stereo input/output in AES/EBU or S/PDIF formats. A total of six inputs/outputs are available for simultaneous record and playback with exceptionally low latency on a large number of supported computer platforms.
0 1
The VX442 can be used with most audio applications using:
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6 simultaneous inputs/outputs.
4 balanced analog inputs/outputs +4 dBu/-10dBV.
Balanced digital stereo input/output in AES/EBU or S/PDIF formats.
Versatility and exceptional audio quality: Sample rate: 8 to 96 kHz. PCM resolution: 8,16 or 24 bit. Excellent audio performance, even at lowest resolution.
Very low latency, due to hardware and software architecture (by default at 48 kHz: 8ms, minimum: 1.5 ms).
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Universal PCI compatibility (3.3V ready)
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text:
CANNES, France, July 29 (UPI) -- The value of the diamond jewels stolen from a luxury hotel in Cannes was raised to $136 million, a record heist for France, a local prosecutor said Monday.
The theft of 34 pieces was carried out by a lone gunman Sunday at the Carlton Intercontinental Hotel on the French Riviera.
Philippe Vique, deputy prosecutor of the nearby town Grasse, which has authority over Cannes, said the theft surpasses the previous French record of about $106 million in a 2008 holdup in Paris, The Wall Street Journal reported. This past weekend's heist had previously been estimated at $53 million.
The gems taken from the Cannes luxury hotel were part of an exhibition of works from the Leviev diamond house, owned by Russian-born Israeli billionaire Lev Leviev. The Journal said a Leviev employee based in London said the company was working with police and insurance companies but declined to confirm the value of the pilfered jewelry.
The latest theft follows a $1.4 million jewelry heist from a hotel safe during the Cannes film festival in May and the theft of a $1.9 million necklace in the nearby city of Antibes also in May, the Journal noted.
Vique said differences in the ways the thefts were carried out seemed to indicate different parties were responsible.
He also said there was no evidence to link the Sunday robbery to the Pink Panthers gang of jewelry thieves. A member of the gang, Milan Poparic, 34, of Bosnia, escaped from a Swiss prison last week, the newspaper said.
Another member of the gang escaped prison in recent months, as well.
The Journal said a police officer familiar with the latest case said investigators were considering the possibility it was an inside job or that there was a security systems failure.


text:
Spoof news snippets from Wednesday 27 April 2011
Ellie James gets Hailed on
Spoof reporter, Ellie James, gets caught in hailstorm days after making fun of Texas Governor Rick Perry and his stupid pray for rain proclamation. "Hope my car is ok," she wonders.
Secretary of State Hillary Clinton Wants A Job Closer To Home
Reports coming out of the White House are that Secretary of State Hillary Clinton is so tired at having to travel so much that she wants to trade jobs with Vice-President Joe Biden.
This Television Advertising Announcement Was Actually True
A furniture store in St. Louis that advertised that it was closing and was holding a "Going Out of Business" sale actually was closing and going out of business.
Two Governors With Two Very, Very Similar Agendas - Hmmm.
Governor Jan Brewer of Arizona and Governor Scott Walker of Wisconsin have apparently been exchanging notes and they will soon be announcing that they will be having their DNA checked.
The Royal Queen Finally Reveals Her Royal Reasons
Queen Elizabeth has revealed why she didn't invite the Obama's to the Royal Wedding. She said that she did not like Michelle giving her a high five and the president asking her "So what's up mama?"
Americans Go Comando
As underwear sales rise, Americans go comando. Mothers throughout America fret about what paremedics would think in case of an accident.
A Tarnished Pope
Pope John Paul II's tarnish expected to come off with baking soda and water.
Palace Admits That Kate Middleton Does Not Exist
'It's all done with CGI and animatronics,' admitted a spokesperson for Buckingham Palace. 'We can't afford to have another one divorcing, getting killed or just generally embarrassing Her Majesty.'
No Sex Please, We're Jihadists
A prisoner in Guantánamo Bay has said that he had to inject himself to make him impotent so that he would not be distracted by women when planning terror attacks - obviously, the Burka does not work!
The Netherlands Is Certainly On The Right Track
In an effort to get its citizens to exercise more the Netherlands has decided to outlaw television remote controls.
Some Sharks Can Swim For Hundreds Of Miles In One Day
A man recently found a shark fossil in a Kentucky coal mine. He noted that a miner's pick axe was found near by.
The Movie Producer Did Not Want To Offend Colonel Gadhafi
The title of the Robert Pattinson film Water For Elephants was originally Whiskey For Camels.
Where Oh Where Is Osama Bin Laden?
Osama Bin Laden was recently spotted in a Best Buy Electronics Store in Karachi, Pakistan purchasing a new video recorder.
The Dallas Cowboys Prepare for Bowl
The Dallas Cowboys prepare for a different kind of bowl by opening the roof and adding 2% milk.
Cameron Uses Four-Letter Words - Three Times!
… "Calm down dear!"
Michael Winner's Furious at Prime Minister's Remarks
Michael Winner claims that he has a super-injunction on saying "those words" that David Cameron uttered today. This in effect means that you can not say them. Ever.
David Cameron Has Got Talent!
By today's performance in Parliament, Prime Minister David Cameron, has demonstrated that he can advertise car insurance. Fellow MPs accuse him of receiving backhanders from Esure
William's Balls
Catherine Middleton's sister, Pippa, has allegedly questioned whether William has his own balls. She thinks the royal orbs are really Gary Glitters.
Charlie Sheen Will Soon Be Receiving 100 Pounds of Japan's Finest Sushi
Japanese leaders today thanked Charlie "Winning" Sheen for getting THEIR meltdown out of the top daily news stories.
"Hey Joe, You'll Never Guess Who You Just Missed"
The latest Osama Bin Laden video shows him holding up two fingers behind Vice-President Joe Biden's head.
LBJ Could Hear Them Whispering From 50 Feet Away
An old recently discovered diary belonging to President Lyndon Baines Johnson shows that he was big on earmarks.
Sarah Palin Has Returned From HiddenLand
Sarah Palin endorses a new plan to use Anthrax-sniffing dogs. She quickly dodges several hundred shoes.
Fishing Boat Nets Torpedo and Firth of Forth
A trawler netted a new torpedo in the Firth of Forth today. It had to be dealt with by Royal Naval bomb disposal teams. However, the MoD deny ownership."We haven't any ships any more!" they said.
Imogen Thomas's singing secret is out
She's was doing gigs all last year...
Health reforms Unpopular
Asked why health reforms were unpopular with 98% of nurses David Cameron said they should wake up reality and look for a really useful job like PR if they are not happy.
Taliban support Royal Wedding
With thousands of military personnel from Afghanistan taking part in a dress rehersal for the Royal Wedding, the Taliban are finding life easier. They will be celebrating the Wedding Day.
Haig and Syria
Foreign Secretary, William Hague says there is still time for Syria to go down the right fork in the road, but they must avoid killing so many people in doing so.
Gove pays Consultants
Government Minister, Michael Gove, has spent £21 million on consultants, it was revealed to-day. But still they don't know why he his breath smells.
Hearing Aid
My husband's got a new hearing aid but he can't hear a word I say. Seems happy though.
Gone Digital
My friends gone digital. He can't keep his hands off other people's wives.
Queen Elizabeth Is Trying To Bond With Kate Middleton
Queen Elizabeth reveals extremely personal Royal Family tea sipping secrets to Kate Middleton.
Butch Patrick's Widow's Peak Still Has It
Butch Patrick, aka Eddie Munster of The Munsters has agreed to guest star on The Vampire Dairies.
The Lesbian Couple Wanted No Part of The Salami
Noted lesbians Melissa Etheridge and Chely Wright have recorded a single called "Hey Fella You Wanna Get That Pecker Away From Me"
Queen Latifah Knows How To Get Down
Queen Latifah to appear in the new Bravo Network police comedy-drama television series Undercover Hotel Ho Ha Ha.
Government Issues Free Medication to the Proletariat
In an effort to appease the antipathy shown by the unemployed, sick, and aged voters - Cameron is to distribute free tubes of 'Antisceptic' Disinfectant, to be taken in conjunction with VAT rises.
Birthers Demand Birth Certificate of Disney's Prince Naveen
Prince Naveen, from Disney's Princess and the Frog is the latest Disney Prince being harassed to provide a birth certificate. Naveen states that being a frog had less controversy.
Latest Gallup Poll Picks Omerosa as Trump's Running Mate
Omerosa, the gal from the Apprentice that everyone loves to hate, is the person America wants to see as Trump's running mate if he gets the GOP nod.
No High Five
A rabid environmental group failed to get a tax on use of excessive energy, whatever that means. All they achieved was to limit their own use of toilet paper and no one will shake their hands!
Smuggle Bathtubs
Israelis help Palestinians to bathe in Gaza, by providing soap. Hamas has been so busy smuggling weapons, dressing in burqas and turning bathtubs into rockets that only one usable bathtub remains!
It's still the Lousy Economy Stupid
South American nations move to dictatorial socialism. In a few years when the populace is still starving, Peron-like right wing dictators will emerge. Then the political cycle will start over again!
There is No Free Lunch
You had to buy a beer to get a free lunch. European economies found this out, South Americans will find this out and President Obama may one day wake up to this fact of life as a one-term president!
Very Late Breaking News
Russia backs tougher nuclear safety rules 25 yrs after Chernobyl. BP will hire competent people 1 yr after the gulf oil spill. Mrs. O'Leary will keep her cow away from Chicago 140 yrs after the fire.
Arrogant Bicyclists Upset
San Francisco CA City Council may pass a law, that bicyclist's using the Golden Gate Bridge can only go 5/10 mph. Motorists say "these scofflaws are a menace during rush hours & cause accidents!"
The Best Laid Plans
USDOT Secretary LaHood orders two air traffic controllers be on duty in a control tower during the graveyard shift. A couple was found sleeping together the next morning, after an emergency landing!
I'm Busy that Day
Anti-monarchy Member of Parliament (MP) turns down invitation to the Royal Wedding. The anonymous MP said we have a dental appointment we waited a year to get!
New US Department of State Questions
If you don't have a birth certificate, some sample questions passport applicants must now answer. Were you baptized or circumcised? Who was present when you were born? Where did your mother work?
American Football Season Should be Interesting
After a federal judge ended a 45-day lockout, small groups of NFL players went to their team facilities. However, the only equipment available was foam rubber footballs & bubble wrap protective gear!
Human Rights Groups Upset with the UN
Suppression of protesters since mid-March has killed more than 400 people across Syria, with 120 dead over the past weekend. Yet, Syria is a frontrunner for a seat on the UN Human Rights Council!
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Port Townsend utility customers to see higher rates
By Charlie Bermant
Peninsula Daily News
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The action approved the hike to come in three increments: in August, next January and January 2015.
The increase in water and sewer rates is needed to pay for a mandated UV filtering system, a reservoir upgrade and other infrastructure improvements, according to city officials.
Councilman Bob Gray opposed the increase, while Mayor David King, Deputy Mayor Kris Nelson, and council members Michelle Sandoval, Deborah Stinson and Catharine Robinson voted for it. Council member Mark Welch was not present.
Gray proposed a town meeting to study the effects of higher utility rates.
"We have experts coming in all the time telling us we can raise the rates, but we don't know what the impact will be," Gray said.
"I suggest that we get some experts in here who can tell the other side, such as how many shutoffs we will have when we have a rate increase."
The council also deferred action on the purchase of an automated metering interface until more information about the system can be gotten.
The utility rate hike will mean an average increase of $12 per household based on the average residential use of 3,000 gallons of water monthly.
The average monthly residential bill for sewer service and 3,000 gallons of water will rise from $80.06 to $92.54 this year.
It will increase to $101.60 in 2014, $108.69 in 2015, $109.82 in 2016, $110.59 in 2017 and $118.88 in 2018.
The increase also includes a $5 monthly surcharge to subsidize capital projects, which could rise as high as $14 in 2018.
That could end up being considerably less if the treatment facility can be built for less than the present projection of $7.1 million — which depends on the city's ability to secure low-interest loans, according to City Manager David Timmons.
Gray said in his objection that the utility rate included allowances for low-income families that were not publicized.
"We only have 4.25 percent of our customers taking advantage of these rates, while our poverty rate is at 13 percent," Gray said.
"For some reason, the word isn't getting out."
There was some discord at the meeting.
King asked whether Gray had "have anything to contribute except delay to this issue," while Gray said that King was the cause of the delay for his recommendation of further study of the metering system.
Said Sandoval: "If we have more delays, that's not going to change what we need to do.
"We still need to do the UV, the reservoir and improve our infrastructure," she said.
"They are not going away."
Timmons said that the delays so far have helped the situation, as the city was able to implement rate increases that were less severe than projected.
This effort was lauded by Nelson, who said she wasn't always convinced that Timmons was on the right track.
"David did a ton of different things. He'd cut a half a million dollars here and squeeze the budget there, and I was one of the first ones to say, 'what are you doing?'" she said.
"I didn't like what he was doing at all, but in the end, he really did save us a lot of money, so I'm sorry I doubted you so much," she told Timmons.
Jefferson County Editor Charlie Bermant can be reached at 360-385-2335 or email@example.com.
Last modified: July 02. 2013 6:12PM


text:
MILWAUKEE — Wisconsin Democratic gubernatorial candidate Mary Burke has released a five-part jobs plan that involves investing in entrepreneurs and focusing on higher education.
The former Trek Bicycle Corp. executive released the plan Tuesday morning. She was expected to discuss it in detail at an afternoon event at Marquette University.
The issue of jobs figures to be key as she tries to unseat Republican Gov. Scott Walker. Walker had pledged during his 2010 campaign to add 250,000 private-sector jobs by the end of this year, a goal that appears unlikely.
Burke says the state should invest in entire industries, not individual companies. She says doing so helps promote the common interests of firms and provide more benefit.
She also wants to lower tuition costs and provide more investment capital for entrepreneurs.


text:
PORTLAND — Luke Babbitt fed the ball to a curling Jonny Flynn, who hit Hasheem Thabeet on a touch-pass for a dunk.
And there, ladies and gentlemen, was your highlight.
Five months ago, one of those players was an appears-in-blowouts-only reserve, while the other two were living in Houston Rocket obscurity. In the Trail Blazers' final home game of the season, however, they were the ones best suited to get the irrationally loyal fans on their feet.
No, Portland did not send send off the Rose Garden faithful with a win. The Jazz coasted to a 112-91 victory after leading by as many as 31 points.
And while you are certainly in your right to label the effort uninspiring, in this perpetually disappointing season, do not call it unfitting.
"Since I've been here, this is the worst season we've had in four years," said Blazers forward Nicolas Batum, adding that, despite his team's futility, the fans' support has not wavered. "It's frustrating because when I came back in December from France and I took my flight ticket to come over and people asked me, 'When are you going to fly back?' I said not before June because I knew we would make the playoffs."
Batum was then asked why this ended up being the worst of his four seasons, a question he admits he and teammates have asked several times with no satisfactory answer.
As to why the Blazers (28-35) lost this particular game? Well, there are certainly some more concrete tangible reasons there.
First of all, Jazz point guard Devin Harris scored 25 of his 27 points in the first half and knocked down 6 of his 10 3-pointers. He came into the game a career .312 shooter from long-distance who had hit just 66 3's on the season. He then added six more before the start of the third quarter.
Blazers interim coach Kaleb Canales said that his team's goal was to try to control Utah's points in the paint, and that getting beaten along the perimeter is sometimes a byproduct of that focus.
Harris did not disagree.
"It does wonders when you're shooting wide-open shots. I had a couple of those that kind of got me going, and it just snowballed from there," Harris said. "Usually when you hit the first couple shots — I think I hit my first three — from there I knew it was going to be a good night."
Utah (33-30) also had exponentially more for which to play than did the Blazers. With the win, the Jazz moved into the eighth and final playoff spot and lead Phoenix and Houston by a half a game apiece.
Then again, with LaMarcus Aldridge, Raymond Felton and Jamal Crawford all sitting on the Blazers bench in suits before the game, it is difficult to visualize a different outcome.
Aldridge shut down his season last week when doctors discovered a labral tear in his hip that will require surgery. Felton likely ended his year Wednesday due to a nagging Achilles tendon. Crawford, meanwhile, was the latest rotation player to sit out for the sake of Portland developing its younger players.
And then there was Batum, who went 1 of 6 in the first half before resting the entire second due to a tender left quad.
"You could see it," Batum said. "I was hurting the team."
One of Canales' motifs since taking over as head coach has been acclaiming the Rose Garden crowd.
That said, the sour taste he left the hometown fans did not appear to peeve him more so than any other loss.
"It's a tough time with that being game one of the first games season or the last game of the season, it's tough any time," Canales said. "I'll continue to say it, I believe it — we do have the best fans in the world. It's been a challenging season for us, but we appreciate what they mean to us."
Portland's starting lineup was as follows: Nolan Smith and Wesley Matthews at guards, Batum and J.J. Hickson at forwards, and Thabeet at center.
Smith logged a season-high 39 minutes and scored 16 points on 7-of-16 shooting. The one glaring statistic? His zero assists.
Flynn, on the other hand, countered with seven dimes, even if he did manage five fewer points than Smith.
After the game, Matthews quipped, "Our starting point guard had as many assists as LaMarcus Aldridge," to which Smith joked back, 'My job is to get it in your guys hands, it's up to you to hit it."
This was said with a smile on Smith's face, but he did admit to being frustrated with his performance.
And it is not like Matthews had any trouble finding the bottom of the net. The third-year guard led Portland with 21 points while hitting three 3-pointers.
Babbitt and Hickson added 16 points apiece.
Portland will play its final three games of the season on the road, starting with the Grizzlies in Memphis Saturday.
Matt Calkins can be contacted at 360-735-4528 or email@example.com


text:
Chef and cookbook author Michael Chiarello gathers family and friends in his organic Napa Valley vineyard over a locally grown, gourmet feast.
13 Holiday Party Tips
Napa Valley chef Michael Chiarello hosts a gourmet Thanksgiving party for friends and family — in the midst of his vineyard.
Get ideas for Thanksgiving side dishes.More
For celebrated chef and cookbook author Michael Chiarello, Thanksgiving means family, friends, and "hopefully, a few Thanksgiving orphans" gathering right in his organic Napa Valley vineyard over a locally grown feast. Chiarello takes his time with the holiday meal, personalizing the day with meaningful dishes and serving pieces. He honors his Italian and California heritage by cooking with seasonal organic products and highlighting handmade objects, many of which he sells at NapaStyle, his kitchen and home retail store.
Pictured: The centerpiece of chef Michael Chiarello's Thanksgiving meal is a free-range "Willie Bird" turkey from Sonoma County, Calif. He brines it with juniper berries and bay leaves, then stuffs the cavity with rosemary and lemons. Guests help choose the spices, like this fennel-spice rub. "The tartness of pomegranate as a garnish complements the turkey," says Chiarello.
"No matter where you live, there are local artisans to support. What could say Thanksgiving better than celebrating the community you live in?"
Stress-Free Thanksgiving Dinner


text:
Artist description:
It's almost 4am. Demi trolled on us again, this time with a blackout ... #ItsALovaticThing
Java Draw Created Feb 11, 2013 640 x 460px Unless noted Copyright 2013 BiaChan.
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so cute!
Adorable. 5/5h
You made it look so doft and cute! And the hair is epic :D
Very nice 5/5
nice colouring on the hair and also cool lineless star


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I want to fuck every single one of them girls O_O
Really liking this hentai.
I looked at the release dates, both episodes 2009-08-03 & 2010-08-03.
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text:
In a rare public outing in what passes for the world of wine in London, founder Tony Laithwaite (seen here) was present at the autumn press tasting of his eponymous company's wares at Altitude, high above the Thames at the top of Millbank Tower last month. The picture shows the rather distracting view of the Houses of Parliament, the London…
Written by 8 Oct 2010
Laithwaites - high-altitude wines?
Annual membershipPerfect for wine lovers £85 annual Join Save £17
Monthly membershipPerfect for wine lovers £8.50 monthly Join
Still unsure?Find out more about membership and the Jancis Robinson Team. Find out more
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text:
Kengo Kuma, Principal, Kengo Kuma & Associates
Please note: This lecture takes place in Wheeler Auditorium on the UC Berkeley campus. Both Wheeler Hall and Wurster Hall are open today and fully operational.
Kengo Kuma's architecture draws on ancient Japanese traditions as well as today's international and innovative technologies. His use of materials might include paper and straw in small countryside villages, or experimental silicon skins in the city. Either way, it is important to him that his architecture be sensual, delighting the eye and inviting our touch. All of this may be out of step with what most architects are doing today, but perhaps that is exactly what makes Kuma such an exciting architect.
This event is co-sponsored by the Center for Japanese Studies, UC Berkeley, and MUJI, and is part of the Fall 2013 Lecture Series at the College of Environmental Design, UC Berkeley. For other lectures in the series, see: http://ced.berkeley.edu/events-media/lecture-series/.


text:
NEALON-L ArchivesArchiver > NEALON > 2010-09 > 1283800017
From: ,
Subject: Re: [NEALON] Nealons of Limerick & Co Clare1800's
Date: Mon, 06 Sep 2010 19:06:57 -0000
This is a Message Board Post that is gatewayed to this mailing list.
Author: jackie0051
Surnames: Scott, Brennan
Classification: queries
Message Board URL:
Message Board Post:
As you say Dennis is not really a common Irish name.
I have a Dennis Nealon born Ireland approx 1834 married to Mary McAndrew.
Living in Dumore Luzurne Nr Scranton Pennsylvania 1870.
Children Mary, Catherine, Nellie and Ambrose.
Dennis died in the war 1870.
Do come back to me if you feel there's a connection and I'll put you in touch with the family's researcher.
Regards Jackie.
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text:
Legal Question in Criminal Law in Virginia
im typing you because the lynchburg police dept is holding a man for some thing he was not charged for its a big case....dealing with a homicide many people are saying it's this man because the man lived next door to where to crime took place can you help? What can he do?
Asked on 6/19/10, 4:57 am
1 Answer from Attorneys
Michael Hendrickson Law Office Michael E. Hendrickson
He should hire himself the best criminal defense counsel he can find and if he cannot afford such on his own, then he should ask the court to appoint one for him (if he has not already done so).
Answered on 6/19/10, 10:49 am
Related Questions & Answers
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What is the punishment for first offence of shoplifting in va. My friend stole some... Asked 6/17/10, 10:24 pm in United States Virginia Criminal Law


text:
Apple doesn't really need commercials to sell you on their products, but their latest Macbook Air ad is notably fresh. The clip features some elements that will be familiar to and appreciated Hip-Hop heads.
The 30-second visual features a knocking track called "Chimes" by producer/DJ Hudson Mohawke, who happens to be signed to Kanye West's G.O.O.D. Music. As viewers listen to the instrumental, the familiar view of the back of a Macbook with the glowing Apple logo cycles threw a myriad of stickers.
Some notables include the logos of Supreme, Fool's Gold, Heisenberg from Breaking Bad and a Beats sticker.
Watch the new spot below. Yes, this post happened to be written on a Macbook Air, but that was only a coincidence.
Photo: YouTube


text:
Morris Area baseball goes to 4-0
Morris Area stayed undefeated this season with a 7-2 win at Minnewaska Area Tuesday.
Both teams had 11 hits, but the Tigers got the most production, starting with Ryan Beyer's two-run home run in the top of the first.
Beyer went 4-for-4, also doubling, and driving in four runs. Tyler Hansen, who was 3-for-4, doubled and scored three times.
Minnewaska stranded 10 runners and the Tigers three.
Shane Bosek and Cory Holten each had a pair of hits for Minnewaska . Bosek tripled and drove in both Lakers' runs.
Eric Riley threw all seven for Morris. He walked four and fanned nine.


text:
Tax Collector
Mary Alex – Tax Collector
Mindy Moore – Deputy Tax Collector
Tel (845)677-3419
Fax (845)677-2085
Email: firstname.lastname@example.com
VIEW YOUR TAX BILL ON-LINE You can view your tax bill on-line by clicking on the following link. You may also download a receipt for a previous year: View Your Tax Bill
The Tax Office is responsible for the collection of property taxes which includes Town, County, Library and Fire Protection. If school or village taxes are unpaid they will be included in the property tax bill.
The tax bill covers the period of January 1 – December 31st.
- January 14-February 28: No interest
- March 1 – March 31: 2% Interest
- April 1 – April 30: 3% Interest
- May 1 – May 31: 4% interest, plus additional $2.00 Fee
- If the due date falls on a weekend, the payment time period is extended to the Monday immediately following the due date.
Unpaid taxes are turned over to the Dutchess County Commissioner of Finance on June 1st. 845-486-2086.
Any changes in address or updates to bank or escrow information must be made in writing to:
Mary Alex, Tax Collector
P.O. Box 667
Millbrook, NY 21545
Fax to: 845-677-2085
I did not receive a tax bill, or I received it late, do I still have to pay a penalty? YES. The failure to mail a statement or the failure of a property owner to receive a bill does not affect the validity of the taxes or interest prescribed by law (NYS Tax Law Section 922). Neither the tax collector nor any other official has the authority to waive statutory penalty charges.
Do you accept payment with credit cards? No. Payments payments can be paid by check or cash.
Can I pay my taxes in installments? The town does not accept partial payments. The bill must be paid in one full payment.
Do I have to pay in person? No, payments may be mailed to the Tax Office. A US Postal Service postmark is used to determine the date the payment was made. We recommend that if you are waiting to the last day to pay your taxes, that you see the postal clerk affix the postmark. A postage machine, such as Pitney Bowes, does not count as a US Postal Service postmark.
How do I have a copy of my tax bill sent to another party? If you are a property owner 65 years or older or have a disability you may complete the New York State Department of Taxation and Finance Form RP-923 to have a copy of your bill or notice of unpaid taxes sent to a third party. Request for a duplicate tax bill
The Board of Assessment granted a deduction in my assessment. Now what? The new assessed value should be first applied on the school tax bill. If the new assessment hasn't been applied to the bill, please contact the Assessor's Office at 845-677-8321. You will be required to complete an App for Corrected Tax Roll.


text: Does anyone know how to put a horizontal line into a document? I need to paste it on top of a graph I copied in from SPSS, so the graph is an image file. This is Word 2003 on Windows XP. Thanks in advance! Elise ************************************************************* You are receiving this mail because you subscribed to mso@xxxxxxxxxxxxx or MicrosoftOffice@xxxxxxxxxxxxxxxx To send mail to the group, simply address it to mso@xxxxxxxxxxxxx To Unsubscribe from this group, send an email to mso-request@xxxxxxxxxxxxx with the word "unsubscribe" (without the quotes) in the subject line. Or, visit the group's homepage and use the dropdown menu. This will also allow you to change your email settings to digest or vacation (no mail). http://www.freelists.org/webpage/mso To be able to use the files section for sharing files with the group, send a request to mso-moderators@xxxxxxxxxxxxx and you will be sent an invitation with instructions. Once you are a member of the files group, you can go here to upload/download files: http://www.smartgroups.com/vault/msofiles *************************************************************


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Roshni.x, luvlife84, Kriyansh, kriyanshfan, kumjoo, sweetshine,
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Roshni...gr8 post...i dun knw y but d dazzler-weakling thing u said made me laugh lolz anyways b4 i go ahead i wud lyk to say yes i'm a crazeee Kriyansh fan but i do luv Swaron nd oder couples tooothe thing wid me is once i get attached to d show i jus cnt hate anything of d show unless d cv's givs a reason for me to hate(lyk dey did in dmg wid SR)sooo till now no hate-factor for D3
cuming to Kriyansh vs Swaron :-
The thing is from the time of elections all of us have been having HIGH hopes to c soo much of Kriyansh becoz it was deir election nd deir fight but wat cv's gave us:
in d first week of d elections we thot there will be full on thakkar scenes of Kriyansh but wat cv's gave us was Swaron thakkar scenes nd Kriyansh luk luk hawa hawa
the second week where we xpected d tapad to occur we hoped it wud be a dream but wat cv's gave us was a reason to hate Sharon nd the tapad becoz of misunderstnding which was depressing but still ppl watched it wid awe coz aftr d tapad dere was lots of aggression from Rey nd jealousy from Kria(in d party) which kind of xcited us nd gave us a hint dat frm now its Kria's turn to realize everything
nd den d last week of elections came d winner for d elections for which ppl guessed weeks bak dat Rey wud be d GS nd Kria his assistant which ofcourse created dhamaka for us Kriyansh fans becoz we were xpecting sooo much of Kriyansh...ab jab sapna toot tha hai tho gussa tho atha hi hai'..dis week ppl had HIGH xpectations from Kriyansh nd wat cv's gave us was Kria falling sick which is ok becoz Shakti was away at dat tym but dnt they lyk pre-shoot d scenes but no instead dey gave Swaron which we were okay wid but dey gave such a high dosage dat even d Swaron fans demselves got irritated becoz even Swaron's story was not really moving anywhere it was d same old luk luk hawa haw and stupid arguments tut tut
now the cv's r DRIVING us sooo MAD dat ppl jus cnt take it anymore'.i mean cv's really need to learn BALANCING
sooo basically wat I'm trying to say is d cv's r d main CULPRITS here'..i mean I dun knw y but I feel dey r jus playing around wid us'.dey knw I'm sure dat mostly ppl r Kriyanshlovers but dey hardly show dem instead dey jus giving a reason to dislike Swaron nd ppl deciding to stop watching d show'..totally beats me wat's up in d cv's little brains
aartii totally understand wat u r trying to say nd its rite coz every person has a rite to voice deir feelings'.lyk ur straight-forward attitude
nd oh sorry jus cudnt resist saying dis aarti nd preety luv both of ur dps
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