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 distributional thesauri based networks from data at different time points and cluster each of them separately to obtain word-centric sense clusters corresponding to the different time points. Subsequently, we compare 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 particular sense has died. We conduct a thorough evaluation of the proposed methodology both manually as well as through comparison with WordNet. Manual evaluation 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 randomly 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 respectively confirmed by WordNet. Our approach can be applied for lexicography, as well as for applications like word sense disambiguation or semantic search.
Word sense disambiguation as well as word sense discovery have both remained key areas of research right from the very early initiatives in natural language processing research. Ide and Veronis present a very concise survey of the history of ideas used in word sense disambiguation; for a recent survey of the state-of-the-art one can refer to @cite_9 . Some of the first attempts to automatic word sense discovery were made by Karen Sp "a rck Jones ; later in lexicography, it has been extensively used as a pre-processing step for preparing mono- and multi-lingual dictionaries @cite_6 @cite_1 . However, as we have already pointed out that none of these works consider the temporal aspect of the problem.
@cite_9 @cite_1 @cite_6
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Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches. The assessment of WSD systems is discussed in the context of the Senseval Semeval campaigns, aiming at the objective evaluation of systems participating in several different disambiguation tasks. Finally, applications, open problems, and future directions are discussed. The chapter deals with word sketches - one-page automatic, corpus-based summaries of a word's grammatical and collocational behaviour. They were first used in the production of the Macmillan English Dictionary and were presented at Euralex 2002. At that point, they only existed for English. Now, we have developed the Sketch Engine, a corpus tool which takes as input a corpus of any language and a corresponding grammar patterns and which generates word sketches for the words of that language. It also generates a thesaurus and 'sketch differences', which specify similarities and differences between near-synonyms. We briefly present a case study investigating applicability of the Sketch Engine to free word-order languages. The results show that word sketches could facilitate lexicographic work in Czech as they have for English. This paper introduces the Word Sketch: a collocation-based resource of proven value for English lexicography. Issues involving the automatic extraction and presentation of salient collocations are discussed. It is further shown how the combination of signicant patterns may lead to even greater precision in the identication of collocations.