Novel Word-sense Identification
Paul F. Cook, Jey Han Lau, Diana McCarthy, Timothy J. Baldwin · 2014
Automatic lexical acquisition has been an active area of research in computational linguistics for over two decades, but the automatic identification of new word-senses has received attention only very recently. Previous work on this topic has been limited by the availability of appropriate evaluation resources. In this paper we present the largest corpus-based dataset of diachronic sense differences to date, which we believe will encourage further work in this area. We then describe several extensions to a state-of-the-art topic modelling approach for identifying new word-senses. This adapted method shows superior performance on our dataset of two different corpus pairs to that of the original method for both: (a) types having taken on a novel sense over time; and (b) the token instances of such novel senses. 1 Novel word-senses The meanings of words change over time with, in particular, established words taking on new senses. For example, the usages of drop, wall, and blow up in the following sentences correspond to relatively-recent senses of these words that appear to be quite common in text related to popular culture, but are not listed in many dictionaries; for example, they are all missing from WordNet 3.0 (Fellbaum, 1998).