Distributional Lexical Entailment by Topic Coherence

Automatic detection of lexical entailment, or hypernym detection, is an important NLP task. Recent hypernym detection measures have been based on the Distributional Inclusion Hypothesis (DIH). This paper assumes that the DIH sometimes fails, and investigates other ways of quantifying the relationship between the cooccurrence contexts of two terms. We consider the top features in a context vector as a topic, and introduce a new entailment detection measure based on Topic Coherence (TC). Our measure successfully detects hypernyms, and a TC-based family of measures contributes to multi-way relation classification.