Abstract
This paper addresses the problem of semantic relation identification for a set of relations difficult to differentiate: near-misses and overlaps. Based on empirical observations on a fairly large dataset of such examples we provide an analysis and a taxonomy of such cases. Using this taxonomy we create various contingency sets of relations. These semantic categories are automatically identified by training and testing three state-of-the-art semantic classifiers employing various feature sets. The results show that in order to identify such near-misses and overlaps accurately, a semantic relation identification system needs to go beyond the ontological information of the two nouns and rely heavily on contextual and pragmatic knowledge.
Original language | English (US) |
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Pages (from-to) | 381-387 |
Number of pages | 7 |
Journal | International Conference Recent Advances in Natural Language Processing, RANLP |
State | Published - 2009 |
Event | International Conference on Recent Advances in Natural Language Processing, RANLP-2009 - Borovets, Bulgaria Duration: Sep 14 2009 → Sep 16 2009 |
Keywords
- Lexical semantics
- Machine learning
- Semantic relations
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Artificial Intelligence
- Electrical and Electronic Engineering