Index of papers in Proc. ACL 2009 that mention
  • lexical semantic
Das, Dipanjan and Smith, Noah A.
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).
lexical semantic is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Bernhard, Delphine and Gurevych, Iryna
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).
lexical semantic is mentioned in 10 sentences in this paper.
Topics mentioned in this paper:
Chang, Kai-min K. and Cherkassky, Vladimir L. and Mitchell, Tom M. and Just, Marcel Adam
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.
lexical semantic is mentioned in 5 sentences in this paper.
Topics mentioned in this paper: