TAILIEUCHUNG - Báo cáo khoa học: "Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs"

We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two properties associated with pattern-based extractions: popularity and productivity. Intuitively, a candidate is popular if it was discovered many times by other instances in the hyponym pattern. A candidate is productive if it frequently leads to the discovery of other instances. Together, these two measures capture not only frequency of occurrence, but also cross-checking that the candidate occurs both near the class name and near other class members. . | Semantic Class Learning from the Web with Hyponym Pattern Linkage Graphs Zornitsa Kozareva DLSI University of Alicante Campus de San Vicente Alicante Spain 03080 zkozareva@ Ellen Riloff Eduard Hovy School of Computing USC Information Sciences Institute University of Utah 4676 Admiralty Way Salt Lake City UT 84112 Marina del Rey CA 90292-6695 riloff@ hovy@ Abstract We present a novel approach to weakly supervised semantic class learning from the web using a single powerful hyponym pattern combined with graph structures which capture two properties associated with pattern-based extractions popularity and productivity. Intuitively a candidate is popular if it was discovered many times by other instances in the hyponym pattern. A candidate is productive if it frequently leads to the discovery of other instances. Together these two measures capture not only frequency of occurrence but also cross-checking that the candidate occurs both near the class name and near other class members. We developed two algorithms that begin with just a class name and one seed instance and then automatically generate a ranked list of new class instances. We conducted experiments on four semantic classes and consistently achieved high accuracies. 1 Introduction Knowing the semantic classes of words . trout is a kind of FISH can be extremely valuable for many natural language processing tasks. Although some semantic dictionaries do exist . Word-Net Miller 1990 they are rarely complete especially for large open classes . classes of people and objects and rapidly changing categories . computer technology . Roark and Charniak 1998 reported that 3 of every 5 terms generated by their semantic lexicon learner were not present in Word-Net. Automatic semantic lexicon acquisition could be used to enhance existing resources such as Word-Net or to produce semantic lexicons for specialized categories or domains. A variety of methods have been developed for .

• Báo cáo khoa học
• Semantic Class Learning from the Web
• Hyponym Pattern Linkage Graphs
• Zornitsa Kozareva
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
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• Natural Language Processing
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