Tiered Clustering to Improve Lexical Entailment
This is an incremental improvement for NLP tasks involving lexical entailment.
The paper tackled the problem of lexical entailment in NLP by clustering words into senses and using multiple context vectors instead of a single vector, finding that this approach offers some improvement to existing algorithms.
Many tasks in Natural Language Processing involve recognizing lexical entailment. Two different approaches to this problem have been proposed recently that are quite different from each other. The first is an asymmetric similarity measure designed to give high scores when the contexts of the narrower term in the entailment are a subset of those of the broader term. The second is a supervised approach where a classifier is learned to predict entailment given a concatenated latent vector representation of the word. Both of these approaches are vector space models that use a single context vector as a representation of the word. In this work, I study the effects of clustering words into senses and using these multiple context vectors to infer entailment using extensions of these two algorithms. I find that this approach offers some improvement to these entailment algorithms.