Mixing in Some Knowledge: Enriched Context Patterns for Bayesian Word Sense Induction
نویسندگان
چکیده
Bayesian topic models have recently been shown to perform well in word sense induction (WSI) tasks. Such models have almost exclusively used bag-of-words features, and failed to attain improvement by including other feature types. In this paper, we investigate the impact of integrating syntactic and knowledge-based features and show that both parametric and non-parametric models consistently benefit from additional feature types. We perform evaluation on the SemEval2010 WSI verb data and show statistically significant improvement in accuracy (p < 0.001) both over the bag-of-words baselines and over the best system that competed in the SemEval2010 WSI task.
منابع مشابه
Bayesian Word Sense Induction
Sense induction seeks to automatically identify word senses directly from a corpus. A key assumption underlying previous work is that the context surrounding an ambiguous word is indicative of its meaning. Sense induction is thus typically viewed as an unsupervised clustering problem where the aim is to partition a word’s contexts into different classes, each representing a word sense. Our work...
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