That’s sick dude!: Automatic identification of word sense change across different timescales

Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, pages 1020–1029, Baltimore, Maryland, USA, June 23-25 2014. c⃝2014 Association for Computational Linguistics That’s sick dude!: Automatic identification of word sense change across different timescales Sunny Mitra1, Ritwik Mitra1, Martin Riedl2, Chris Biemann2, Animesh Mukherjee1, Pawan Goyal1 1Dept. of Computer Science and Engineering, Indian Institute of Technology Kharagpur, India – 721302 2 FG Language Technology, Computer Science Department, TU Darmstadt, Germany 1{sunnym,ritwikm,animeshm,pawang}@cse.iitkgp.ernet.in 2{riedl,biem}@cs.tu-darmstadt.de Abstract In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. We construct distribu- tional thesauri based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the differ- ent time points. Subsequently, we com- pare these sense clusters of two different time points to find if (i) there is birth of a new sense or (ii) if an older sense has got split into more than one sense or (iii) if a newer sense has been formed from the joining of older senses or (iv) if a partic- ular sense has died. We conduct a thor- ough evaluation of the proposed method- ology both manually as well as through comparison with WordNet. Manual eval- uation indicates that the algorithm could correctly identify 60.4% birth cases from a set of 48 randomly picked samples and 57% split/join cases from a set of 21 ran- domly picked samples. Remarkably, in 44% cases the birth of a novel sense is attested by WordNet, while in 46% cases and 43% cases split and join are respec- tively confirmed by WordNet. Our ap- proach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search. 1 Introduction Two of the fundamental components of a natu- ral language communication are word sense dis- covery (Jones, 1986) and word sense disambigua- tion (Ide and Veronis, 1998). While discovery corresponds to acquisition of vocabulary, disam- biguation forms the basis of understanding. These two aspects are not only important from the per- spective of developing computer applications for natural languages but also form the key compo- nents of language evolution and change. Words take different senses in different contexts while appearing with other words. Context plays a vital role in disambiguation of word senses as well as in the interpretation of the actual mean- ing of words. For instance, the word “bank” has several distinct interpretations, including that of a “financial institution” and the “shore of a river.” Automatic discovery and disambiguation of word senses from a given text is an important and chal- lenging problem which has been extensively stud- ied in the literature (Jones, 1986; Ide and Vero- nis, 1998; Sch¨utze, 1998; Navigli, 2009). How- ever, another equally important aspect that has not been so far well investigated corresponds to one or more changes that a word might undergo in its sense. This particular aspect …

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