University of Cambridge > Talks.cam > Natural Language Processing Reading Group > A Structured Vector Space Model for Word Meaning in Context

A Structured Vector Space Model for Word Meaning in Context

Add to your list(s) Download to your calendar using vCal

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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2017 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity