LexSemTm: A Semantic Dataset Based on All-words Unsupervised Sense Distribution Learning

Abstract

There has recently been a lot of interest in unsupervised methods for learning sense distributions, particularly in applications where sense distinctions are needed. This paper analyses a state-of-the-art method for sense distribution learning, and optimises it for application to the entire vocabulary of a given language. The optimised method is then used to produce LexSemTM: a sense frequency and semantic dataset of unprecedented size, spanning approximately 88% of polysemous, English simplex lemmas, which is released as a public resource to the community. Finally, the quality of this data is investigated, and the LexSemTM sense distributions are shown to be superior to those based on the W..