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| Abstract |
This paper proposes a method which constructs a hierarchy of words from given a set of documents automatically and dynamically. The hierarchy of the words is constructed as a hypernym relation that is... defined by the document frequencies and co-occurrence probability of words. The hierarchy is obtained not only to the whole set of documents, but also to any subset of documents. A typical example of such documents is the search results of a keyword. The hierarchy obtained to this set is the hierarchy of related words of the keywords. Empirical evaluations are conducted for the word hierarchy derived from "Eijiro", an English-Japanese dictionary which contains 1,648,628 descriptions of words. The hierarchies are compared with the proposed method, Niwa's method and Shrinivasan's method with respect to coverage and granularity.
This paper proposes a method which constructs a hierarchy of words from given a set of documents automatically and dynamically. The hierarchy of the words is constructed as a hypernym relation that is defined by the document frequencies and co-occurrence probability of words. The hierarchy is obtained not only to the whole set of documents, but also to any subset of documents. A typical example of such documents is the search results of a keyword. The hierarchy obtained to this set is the hierarchy of related words of the keywords. Empirical evaluations are conducted for the word hierarchy derived from "Eijiro", an English-Japanese dictionary which contains 1,648,628 descriptions of words. The hierarchies are compared with the proposed method, Niwa's method and Shrinivasan's method with respect to coverage and granularity.show more
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