Composition in distributional models of semantics
Distributional models of semantics have proven themselves invaluable both in cognitive modelling of semantic phenomena and also in practical applications. For example, they have been used to model judgments of semantic similarity (McDonald, 2000) and association (Denhire and Lemaire, 2004; Griffiths et al., 2007) and have been shown to achieve human level performance on synonymy tests (Landuaer and Dumais, 1997; Griffiths et al., 2007) such as those included in the Test of English as Foreign Language (TOEFL). This ability has been put to practical use in automatic thesaurus extraction (Grefenstette, 1994). However, while there has been a considerable amount of research directed at the most effective ways of constructing representations for individual words, the representation of larger constructions, e.g., phrases and sentences, has received relatively little attention. In this thesis we examine this issue of how to compose meanings within distributional models of semantics to form representations of multi-word structures. Natural language data typically consists of such complex structures, rather than just individual isolated words. Thus, a model of composition, in which individual word meanings are combined
Mitchell, Jeffrey John
semantics ; computational linguistics ; composition
Composition in distributional models of semantics. Distributional models of semantics have proven themselves invaluable both in cognitive modelling of semantic phenomena and also in practical applications. For example, they have been used to model judgments of semantic similarity (McDonald, 2000) and association (Denhire and Lemaire, 2004; Griffiths et al., 2007) and have been shown to achieve human level performance on synonymy tests (Landuaer and Dumais, 1997; Griffiths et al., 2007) such as those included in the Test of English as Foreign Language (TOEFL). This ability has been put to practical use in automatic thesaurus extraction (Grefenstette, 1994). However, while there has been a considerable amount of research directed at the most effective ways of constructing representations for individual words, the representation of larger constructions, e.g., phrases and sentences, has received relatively little attention. In this thesis we examine this issue of how to compose meanings within distributional models of semantics to form representations of multi-word structures. Natural language data typically consists of such complex structures, rather than just individual isolated words. Thus, a model of composition, in which individual word meanings are combined
composition distributional model semantics distributional model semantics proven invaluable cognitive modelling semantic phenomenon also practical_application example use model judgment semantic similarity association show achieve human level performance synonymy test include test english foreign_language ability put practical use automatic extraction however considerable_amount direct effective_way construct representation individual word representation large construction phrase sentence receive_relatively little_attention examine issue compose meaning within distributional model semantics form representation multi_word structure natural_language data typically consist complex structure rather_individual isolated word thus model composition individual word_meaning combine