Paper: Predicting the relevance of distributional semantic similarity with contextual information

ACL ID P14-1045
Title Predicting the relevance of distributional semantic similarity with contextual information
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Using distributional analysis methods to compute semantic proximity links be- tween words has become commonplace in NLP. The resulting relations are often noisy or difficult to interpret in general. This paper focuses on the issues of eval- uating a distributional resource and filter- ing the relations it contains, but instead of considering it in abstracto, we focus on pairs of words in context. In a dis- course, we are interested in knowing if the semantic link between two items is a by- product of textual coherence or is irrele- vant. We first set up a human annotation of semantic links with or without contex- tual information to show the importance of the textual context in evaluating the rele- vance of semantic similarity, and to assess the prevalence of actual semantic relations betw...