Abstract | The model cleanly incorporates both syntax and lexical semantics using quasi-synchronous dependency grammars (Smith and Eisner, 2006). |
Conclusion | In this paper, we have presented a probabilistic model of paraphrase incorporating syntax, lexical semantics , and hidden loose alignments between two sentences’ trees. |
Experimental Evaluation | We removed the lexical semantics component of the QG,10 and disallowed the syntactic configurations one by one, to investigate which components of mg contributes to system performance. |
Experimental Evaluation | The lexical semantics component is critical, as seen by the drop in accuracy from the table (without this component, pQ behaves almost like the “all p” baseline). |
Introduction | Because dependency syntax is still only a crude approximation to semantic structure, we augment the model with a lexical semantics component, based on WordNet (Miller, 1995), that models how words are probabilistically altered in generating a paraphrase. |
Introduction | This combination of loose syntax and lexical semantics is similar to the “Jeopardy” model of Wang et al. |
QG for Paraphrase Modeling | (2007) in treating the correspondences as latent variables, and in using a WordNet—based lexical semantics model to generate the target words. |
QG for Paraphrase Modeling | 5 We use log-linear models three times: for the configuration, the lexical semantics class, and the word. |
QG for Paraphrase Modeling | WordNet relation(s) The model next chooses a lexical semantics relation between 3360-) and the yet-to-be-chosen word ti (line 12). |
Abstract | In this paper, we propose to use as a parallel training dataset the definitions and glosses provided for the same term by different lexical semantic resources. |
Abstract | We compare monolingual translation models built from lexical semantic resources with two other kinds of datasets: manually-tagged question reformulations and question-answer pairs. |
Conclusion and Future Work | We have presented three datasets for training statistical word translation models for use in answer finding: question-answer pairs, manually-tagged question reformulations and glosses for the same term extracted from several lexical semantic resources. |
Conclusion and Future Work | question-answer pairs, and external knowledge, as contained in lexical semantic resources. |
Introduction | We use the definitions and glosses provided for the same term by different lexical semantic resources to automatically train the translation models. |
Introduction | This approach has been very recently made possible by the emergence of new kinds of lexical semantic and encyclopedic resources such as Wikipedia and Wiktionary. |
Parallel Datasets | 3.2 Lexical Semantic Resources |
Parallel Datasets | Glosses and definitions for the same lexeme in different lexical semantic and encyclopedic resources can actually be considered as near-paraphrases, since they define the same terms and hence have |
Related Work | We henceforth propose a new approach for building monolingual translation models relying on domain-independent lexical semantic resources. |
Related Work | Knowledge-based measures rely on lexical semantic resources such as WordNet and comprise path length based measures (Rada et al., 1989) and concept vector based measures (Qiu and Frei, 1993). |
Brain Imaging Experiments on Adj ec-tive-Noun Comprehension | 4.1 Lexical Semantic Representation |
Brain Imaging Experiments on Adj ec-tive-Noun Comprehension | The lexical semantic representation for strong and dog. |
Introduction | How humans represent meanings of individual words and how lexical semantic knowledge is combined to form complex concepts are issues fundamental to the study of human knowledge. |
Introduction | Given these early succesess in using fMRI to discriminate categorial information and to model lexical semantic representations of individual words, it is interesting to ask whether a similar approach can be used to study the representation of adjective-noun phrases. |
Introduction | In section 4, we discuss a vector-based approach to modeling the lexical semantic knowledge using word occurrence measures in a text corpus. |