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A Structured Vector Space Model for Word Meaning in Context

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If you have a question about this talk, please contact Diarmuid Ó Séaghdha.

At this session of the NLIP Reading Group we’ll be discussing the following paper:

Katrin Erk and Sebastian Padó. 2008. A Structured Vector Space Model for Word Meaning in Context. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP-08).

Abstract: We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account. We present a novel structured vector space model that addresses these issues by incorporating the selectional preferences for argument positions. This makes it possible to integrate syntax into the computation of word meaning in context. In addition, the model performs at and above the state of the art for modeling the contextual adequacy of paraphrases.

This talk is part of the Natural Language Processing Reading Group series.

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