1. At a computer system including one or more processors and system memory, a method for identifying key phrases within a document, the method comprising: an act of accessing a document; an act of calculating the frequency of occurrence of a plurality of different textual phrases within the document, each textual phrase including one or more individual words of a specified language, and outputting a list that contains at least some of the textual phrases together with the frequency of occurrence of output phrases within the document; an act of accessing a language model for the specified language, the language model defining expected frequencies of occurrence at least for individual words of the specified language; for each textual phrase in the output list, an act of determining the expected frequency of each such textual phrase by using the expected frequencies of at least some of the individual words of the language model to interpolate from the expected frequencies of said at least some individual words the expected frequency of each such textual phrase contained in said list; for each textual phrase in the output list, an act of computing a cross-entropy value computed from the frequency of occurrence of the textual phrase within the document and the determined expected frequency of occurrence of the textual phrase within the language model; an act of selecting a specified number of key textual phrases based on the computed cross-entropy values; and an act of populating a key phrase data structure with data representative of each of the selected specified number of key textual phrases.
1. At a computer system including one or more processors and system memory, a method for identifying key phrases within a document, the method comprising: an act of accessing a document; an act of calculating the frequency of occurrence of a plurality of different textual phrases within the document, each textual phrase including one or more individual words of a specified language, and outputting a list that contains at least some of the textual phrases together with the frequency of occurrence of output phrases within the document; an act of accessing a language model for the specified language, the language model defining expected frequencies of occurrence at least for individual words of the specified language; for each textual phrase in the output list, an act of determining the expected frequency of each such textual phrase by using the expected frequencies of at least some of the individual words of the language model to interpolate from the expected frequencies of said at least some individual words the expected frequency of each such textual phrase contained in said list; for each textual phrase in the output list, an act of computing a cross-entropy value computed from the frequency of occurrence of the textual phrase within the document and the determined expected frequency of occurrence of the textual phrase within the language model; an act of selecting a specified number of key textual phrases based on the computed cross-entropy values; and an act of populating a key phrase data structure with data representative of each of the selected specified number of key textual phrases. 4. The method as recited in claim 1 , wherein the act of selecting a specified number of key textual phrases comprises an act of using a threshold functional to determine that one or more of the textural phrases contained in the output list are not statistically significant.