The acquisition and modelling of lexical knowledge : a corpus-based investigation of systematic polysemy
This thesis deals with the acquisition and probabilistic modelling of lexical knowledge. A considerable body of work in lexical semantics concentrates on describing and representing systematic polysemy, i.e., the regular and predictable meaning alternations certain classes of words are subject to. Although the prevalence of the phenomenon has been long recognized, systematic empirical studies of regular polysemy are largely absent, both with respect to the acquisition of systematic polysemous lexical units and the disambiguation of their meaning. The present thesis addresses both tasks. First, we use insights from linguistic theory to guide and structure the acquisition of systematically polysemous units from domain independent wide-coverage text. Second, we constrain ambiguity by developing a probabilistic framework which provides a ranking on the range of meanings for systematically polysemous words in the absence of discourse context. We focus on meaning alternations with syntactic effects and exploit the correspondence between meaning and syntax to inform the acquisition process. The acquired information is useful for empirically testing and validating linguistic generalizations, extending their coverage
Lapata, Maria
The acquisition and modelling of lexical knowledge : a corpus-based investigation of systematic polysemy. This thesis deals with the acquisition and probabilistic modelling of lexical knowledge. A considerable body of work in lexical semantics concentrates on describing and representing systematic polysemy, i.e., the regular and predictable meaning alternations certain classes of words are subject to. Although the prevalence of the phenomenon has been long recognized, systematic empirical studies of regular polysemy are largely absent, both with respect to the acquisition of systematic polysemous lexical units and the disambiguation of their meaning. The present thesis addresses both tasks. First, we use insights from linguistic theory to guide and structure the acquisition of systematically polysemous units from domain independent wide-coverage text. Second, we constrain ambiguity by developing a probabilistic framework which provides a ranking on the range of meanings for systematically polysemous words in the absence of discourse context. We focus on meaning alternations with syntactic effects and exploit the correspondence between meaning and syntax to inform the acquisition process. The acquired information is useful for empirically testing and validating linguistic generalizations, extending their coverage
acquisition model_lexical knowledge corpus_based investigation systematic polysemy deal acquisition probabilistic model_lexical knowledge considerable_body work lexical_semantics concentrate describe represent systematic polysemy regular predictable meaning alternation certain class word subject although prevalence phenomenon long recognize systematic empirical_study regular polysemy largely_absent respect acquisition systematic polysemous lexical unit disambiguation meaning present address task first use insight linguistic_theory guide structure acquisition systematically polysemous unit domain independent wide coverage text second constrain ambiguity develop probabilistic framework provide ranking range_meaning systematically polysemous word absence discourse context focus meaning alternation syntactic effect exploit correspondence mean syntax inform acquisition_process acquire information useful empirically test validate linguistic generalization extend coverage