Chinese input methods are used to convert pinyin sequence or other Latin encoding systems into Chinese character sentences. For more effective pinyin-to-character conversion, typical Input Method Engines (IMEs) rely on a predefined vocabulary that demands manually maintenance on schedule. For the purpose of removing the inconvenient vocabulary setting, this work focuses on automatic wordhood acquisition by fully considering that Chinese inputting is a free human-computer interaction procedure. Instead of strictly defining words, a loose word likelihood is introduced for measuring how likely a character sequence can be a user-recognized word with respect to using IME. Then an online algorithm is proposed to adjust the word likelihood or generate new words by comparing user true choice for inputting and the algorithm prediction. The experimental results show that the proposed solution can agilely adapt to diverse typings and demonstrate performance approaching highly-optimized IME with fixed vocabulary.
@cite_14: In stochastic language modeling, backing-off is a widely used method to cope with the sparse data problem. In case of unseen events this method backs off to a less specific distribution. In this paper we propose to use distributions which are especially optimized for the task of backing-off. Two different theoretical derivations lead to distributions which are quite different from the probability distributions that are usually used for backing-off. Experiments show an improvement of about 10 in terms of perplexity and 5 in terms of word error rate.
@cite_26: 
@cite_9: The most popular type of input method in Japan is kana-kanji conversion, conversion from a string of kana to a mixed kanjikana string. However there is no study using discriminative methods like structured SVMs for kana-kanji conversion. One of the reasons is that learning a discriminative model from a large data set is often intractable. However, due to progress of recent researches, large scale learning of discriminative models become feasible in these days. In the present paper, we investigate whether discriminative methods such as structured SVMs can improve the accuracy of kana-kanji conversion. To the best of our knowledge, this is the first study comparing a generative model and a discriminative model for kana-kanji conversion. An experiment revealed that a discriminative method can improve the performance by approximately 3 .
@cite_21: Chinese spelling check is an important component for many NLP applications, including word processor and search engines. However, compared to checkers for alphabetical languages (e.g., English or French), Chinese spelling checkers are more difficult to develop, because there are no word boundaries in Chinese writing system, and errors may be caused by various Chinese input methods. In this paper, we proposed a novel method to Chinese spelling checking. Our approach involves error detection and correction based on the phrasal statistical machine translation framework. The results show that the proposed system achieves significantly better accuracy in error detecting and more satisfactory performance in error correcting.
@cite_1: Chinese Pinyin input methods are very important for Chinese language processing. In many cases, users may make typing errors. For example, a user wants to type in "shenme" (???, meaning "what" in English) but may type in "shenem" instead. Existing Pinyin input methods fail in converting such a Pinyin sequence with errors to the right Chinese words. To solve this problem, we developed an efficient error-tolerant Pinyin input method called "CHIME" that can handle typing errors. By incorporating state-of-the-art techniques and language-specific features, the method achieves a better performance than state-of-the-art input methods. It can efficiently find relevant words in milliseconds for an input Pinyin sequence.
@cite_0: Chinese input is one of the key challenges for Chinese PC users. This paper proposes a statistical approach to Pinyin-based Chinese input. This approach uses a trigram-based language model and a statistically based segmentation. Also, to deal with real input, it also includes a typing model which enables spelling correction in sentence-based Pinyin input, and a spelling model for English which enables modeless Pinyin input.
@cite_24: Spelling check identifies incorrect writing words in documents. For the reason of input methods, Chinese spelling check is much different from English and it is still a challenging work. For the past decade years, most of the methods in detecting errors in documents are lexicon-based or probability-based, and much progress are made. In this paper, we propose a new method in Chinese spelling check by using maximum entropy (ME). Experiment shows that by importing a large raw corpus, maximum entropy can build a well-trained model to detect spelling errors in Chinese documents.
@cite_27: Most foreign names are transliterated into Chinese, Japanese or Korean with approximate phonetic equivalents. The transliteration is usually achieved through intermediate phonemic mapping. This paper presents a new framework that allows direct orthographical mapping (DOM) between two different languages, through a joint source-channel model, also called n-gram transliteration model (TM). With the n-gram TM model, we automate the orthographic alignment process to derive the aligned transliteration units from a bilingual dictionary. The n-gram TM under the DOM framework greatly reduces system development effort and provides a quantum leap in improvement in transliteration accuracy over that of other state-of-the-art machine learning algorithms. The modeling framework is validated through several experiments for English-Chinese language pair.
@cite_23: Since Japanese and Chinese languages have too many characters to be input directly using a standard keyboard, input methods for these languages that enable users to input the characters are required. Recently, input methods based on statistical models have become popular because of their accuracy and ease of maintenance. Most of them adopt word-based models because they utilize word-segmented corpora to train the models. However, such word-based models suffer from unknown words because they cannot convert words correctly which are not in corpora. To handle this problem, we propose a character-based model that enables input methods to convert unknown words by exploiting character-aligned corpora automatically generated by a monotonic alignment tool. In addition to the character-based model, we propose an ensemble model of both character-based and word-based models to achieve higher accuracy. The ensemble model combines these two models by linear interpolation. All of these models are based on joint source channel model to utilize rich context through higher order joint n-gram. Experiments on Japanese and Chinese datasets showed that the character-based model performs reasonably and the ensemble model outperforms the word-based baseline model. As a future work, the effectiveness of incorporating large raw data should be investigated.
@cite_2: Chinese spelling check (CSC) is still an open problem today. To the best of our knowledge, language modeling is widely used in CSC because of its simplicity and fair predictive power, but most systems only use the conventional n-gram models. Our work in this paper continues this general line of research by further exploring different ways to glean extra semantic clues and Web resources to enhance the CSC performance in an unsupervised fashion. Empirical results demonstrate the utility of our CSC system.
@cite_16: 
@cite_10: We present a discriminative structureprediction model for the letter-to-phoneme task, a crucial step in text-to-speech processing. Our method encompasses three tasks that have been previously handled separately: input segmentation, phoneme prediction, and sequence modeling. The key idea is online discriminative training, which updates parameters according to a comparison of the current system output to the desired output, allowing us to train all of our components together. By folding the three steps of a pipeline approach into a unified dynamic programming framework, we are able to achieve substantial performance gains. Our results surpass the current state-of-the-art on six publicly available data sets representing four different languages.
@cite_12: Kana-Kanji conversion is known as one of the representative applications of Natural Language Processing (NLP) for the Japanese language. The N-pos model, presenting the probability of a Kanji candidate sequence by the product of bi-gram Part-of-Speech (POS) probabilities and POS-to-word emission probabilities, has been successfully applied in a number of well-known Japanese Input Method Editor (IME) systems. However, since N-pos model is an approximation of n-gram word-based language model, important word-to-word collocation information are lost during this compression and lead to a drop of the conversion accuracies. In order to overcome this problem, we propose ways to improve current N-pos model. One way is to append the highfrequency collocations and the other way is to sub-categorize the huge POS sets to make them more representative. Experiments on large-scale data verified our proposals.
@cite_20: We present a discriminative substring decoder for transliteration. This decoder extends recent approaches for discriminative character transduction by allowing for a list of known target-language words, an important resource for transliteration. Our approach improves upon Sherif and Kondrak's (2007b) state-of-the-art decoder, creating a 28.5 relative improvement in transliteration accuracy on a Japanese katakana-to-English task. We also conduct a controlled comparison of two feature paradigms for discriminative training: indicators and hybrid generative features. Surprisingly, the generative hybrid outperforms its purely discriminative counterpart, despite losing access to rich source-context features. Finally, we show that machine transliterations have a positive impact on machine translation quality, improving human judgments by 0.5 on a 4-point scale.
@cite_11: It is very import for Chinese language processing with the aid of an efficient input method engine (IME), of which pinyinto-Chinese (PTC) conversion is the core part. Meanwhile, though typos are inevitable during user pinyin inputting, existing IMEs paid little attention to such big inconvenience. In this paper, motivated by a key equivalence of two decoding algorithms, we propose a joint graph model to globally optimize PTC and typo correction for IME. The evaluation results show that the proposed method outperforms both existing academic and commercial IMEs.
Early work on IMEs include trigram statistical language model @cite_0 and joint source-channel model @cite_27 that is applicable to Chinese, Japanese and Korean languages. Recent studies pay more attention on IME input typo correction @cite_1 @cite_11 along with various approaches on Chinese spell checking @cite_2 @cite_24 @cite_21 . There are also stuides on Janpanese IME which focus on kana-kanji conversion, including probabilistic language model based method @cite_16 , discriminative method @cite_10 @cite_20 @cite_9 , improved @math -pos model @cite_12 , and ensemble method @cite_23 . However, all the above are based on predefined vocabulary and did not exploit the interactive property of IME inputting. @cite_14 and @cite_26 tried to address unknown word problem, but they still require an initial vocabulary or a segmented training corpus.