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 op-timises it for application to the entire vocabulary of a given language. The optimised method is then used to produce L EX S EM TM: a sense frequency and semantic dataset of unprecedented size, spanning approximately 88% of polyse-mous, English simplex lemmas, which is released as a public resource to the community. Finally, the quality of this data is investigated, and the L EX S EM TM sense distributions are shown to be superior to those based on the W ORD N ET first sense for lemmas missing from S EM C OR , and at least on par with S EM C OR -based distributions otherwise.