tailieunhanh - Báo cáo khoa học: "Expressing Implicit Semantic Relations without Supervision"

We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X : Y with some unspecified semantic relations, the corresponding output list of patterns P1 , , Pm is ranked according to how well each pattern Pi expresses the relations between X and Y . For example, given X = ostrich and Y = bird , the two highest ranking output patterns are “X is the largest Y” and “Y such as the X”. | Expressing Implicit Semantic Relations without Supervision Peter D. Turney Institute for Information Technology National Research Council Canada M-50 Montreal Road Ottawa Ontario Canada K1A 0R6 Abstract We present an unsupervised learning algorithm that mines large text corpora for patterns that express implicit semantic relations. For a given input word pair X Y with some unspecified semantic relations the corresponding output list of patterns Ợì . im is ranked according to how well each pattern p expresses the relations between X and Y . For example given X ostrich and Y bird the two highest ranking output patterns are X is the largest Y and Y such as the X . The output patterns are intended to be useful for finding further pairs with the same relations to support the construction of lexicons ontologies and semantic networks. The patterns are sorted by pertinence where the pertinence of a pattern P for a word pair X Y is the expected relational similarity between the given pair and typical pairs for Pi . The algorithm is empirically evaluated on two tasks solving multiple-choice SAT word analogy questions and classifying semantic relations in noun-modifier pairs. On both tasks the algorithm achieves state-of-the-art results performing significantly better than several alternative pattern ranking algorithms based on tf-idf. 1 Introduction In a widely cited paper Hearst 1992 showed that the lexico-syntactic pattern Y such as the X can be used to mine large text corpora for word pairs X Y in which X is a hyponym type of Y. For example if we search in a large corpus using the pattern Y such as the X and we find the string bird such as the ostrich then we can infer that ostrich is a hyponym of bird . Ber-land and Charniak 1999 demonstrated that the patterns Y s X and X of the Y can be used to mine corpora for pairs X Y in which X is a meronym part of Y . wheel of the car . Here we consider the inverse of this problem Given a word pair X