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Automatic Extraction of Examples for Word Sense Disambiguation

Automatic Extraction of Examples for Word Sense Disambiguation

List of

List of Figures 2.1 Supervised machine learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2 General architecture of an memory-based learning system (Daelemans et al., 2007). 29 4.1 Screenshot from Open Mind Word Expert (Chklovski and Mihalcea, 2002). . . . . 41 6.1 Our semi-supervised WSD system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.2 A screenshot of the result from the online interface of the Cambridge Advanced Learner’s Dictionary when searched for the word activate. . . . . . . . . . . . . . . 51 6.3 A screenshot of the result from the online interface of WordNet when searched for the word activate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 7.1 Accuracy change of the adjective hot. . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 7.2 Accuracy change of the noun arm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 7.3 Accuracy change of the verb appear. . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 9

Chapter 1 Introduction Ambiguity is one of the characteristics in all human languages and at the same time it is a prob- lem to be solved in the studies of Computational Linguistics. It is represented by the uncertainty of meaning (if something can be understood in two or more senses) and thus requires a deeper linguistic processing of the natural language. Nowadays it is easy to answer the question ”Who understands language better - computers or human beings?”. One of the reasons why computers still cannot compete with the human cognition is exactly the fact that ambiguity prevails in any natural language text. Thus, for the last few decades the problem has started to gain interest in the CL society and as Ide and Véronis (1998b) mention, proved to be a key to the solutions for other important areas of Natu- ral Language Processing - Information Retrieval, Machine Translation, Information Extraction, Parsing, Text Mining, Semantic Interpretation, Lexicography, Content and Thematic Analysis, Grammatical Analysis, Speech Processing, Knowledge Acquisition and many others. Ambiguity can be present in many ways (e.g structural - when it appears in a sentence or a clause, lexical - when it appears in respect to just a single word). Lexical ambiguity is the problem that Word Sense Disambiguation (WSD) is concerned with and has already tackled to a great extend. Human beings resolve lexical ambiguity by looking in the dictionary, while WSD tries to automate this process. Its task is to automatically assign the correct sense of an ambiguous word dependent on the context in which it can be found. However, by now there has not been found a perfect solution to the problem. Ide and Véronis (1998b) describe many of the still open questions in WSD - How important is the context of the word that is disambiguated?, Which is the optimal choice of possible senses for the target word?, How to compare all different systems and their results?, How much data is needed in order good results to be achieved?, How to provide data for data-driven approaches? Those and many other problems have been considered an issue of great importance from many computational linguists, which is easily proven by the rapidly increasing number of papers in the ACL Anthology 1 where the term word sense disambiguation is mentioned - approximately 1 http://www.aclweb.org/anthology-new/ 10

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