Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity

Co-occurrence Retrieval: A Flexible Framework for Lexical Distributional Similarity

Techniques that exploit knowledge of distributional similarity between words have been proposed in many areas of Natural Language Processing. For example, in language modeling, the sparse data problem can be alleviated by estimating the probabilities of unseen co-occurrences of events from the probabilities of seen co-occurrences of similar events. In other applications, distributional similari...

Robust Co-occurrence Quantification for Lexical Distributional Semantics

Previous optimisations of parameters affecting the word-context association measure used in distributional vector space models have focused either on highdimensional vectors with hundreds of thousands of dimensions, or dense vectors with dimensionality of few hundreds; but dimensionality of few thousands is often applied in compositional tasks as it is still computationally feasible and does no...

Directional distributional similarity for lexical inference

Distributional word similarity is most commonly perceived as a symmetric relation. Yet, directional relations are abundant in lexical semantics and in many Natural Language Processing (NLP) settings that require lexical inference, making symmetric similarity measures less suitable for their identification. This paper investigates the nature of directional (asymmetric) similarity measures that a...

Directional Distributional Similarity for Lexical Expansion

Distributional word similarity is most commonly perceived as a symmetric relation. Yet, one of its major applications is lexical expansion, which is generally asymmetric. This paper investigates the nature of directional (asymmetric) similarity measures, which aim to quantify distributional feature inclusion. We identify desired properties of such measures, specify a particular one based on ave...

A General Framework for Distributional Similarity