Attention, intentions, and the structure of discourse
Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Text understanding with GETARUNS for Q/A and summarization
TextMean '04 Proceedings of the 2nd Workshop on Text Meaning and Interpretation
Semantic normalisation: a framework and an experiment
IWCS-8 '09 Proceedings of the Eighth International Conference on Computational Semantics
A statistics-based semantic textual entailment system
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
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We present VENSES, a linguistically-based approach for semantic inference which is built around a neat division of labour between two main components: a grammatically-driven subsystem which is responsible for the level of predicate-arguments well-formedness and works on the output of a deep parser that produces augmented head-dependency structures. A second subsystem fires allowed logical and lexical inferences on the basis of different types of structural transformations intended to produce a semantically valid meaning correspondence. In the current challenge, we produced a new version of the system, where we do away with grammatical relations and only use semantic roles to generate weighted scores. We also added a number of additional modules to cope with fine-grained inferential triggers which were not present in previous dataset. Different levels of argumenthood have been devised in order to cope with semantic uncertainty generated by nearly-inferrable Text-Hypothesis pairs where the interpretation needs reasoning. RTE3 has introduced texts of paragraph length: in turn this has prompted us to upgrade VENSES by the addition of a discourse level anaphora resolution module, which is paramount to allow entailment in pairs where the relevant portion of text contains pronominal expressions. We present the system, its relevance to the task at hand and an evaluation.