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Automatic Extraction of Examples for Word Sense Disambiguation

Automatic Extraction of Examples for Word Sense Disambiguation


CHAPTER 1. INTRODUCTION 11 850 times. The advances in different aspects of WSD have resulted in a collection of expert views in the field - (Agirre and Edmonds, 2007). The book reviews basic questions like the nature of word senses - (Kilgarriff, 2007), (Ide and Wilks, 2007); discusses different WSD methods - (Mihalcea, 2007), (Pedersen, 2007), (Màrquez et al., 2007); examines evaluation techniques of automatic word sense disambiguation (Palmer et al., 2007), knowledge sources for WSD - (Agirre and Stevenson, 2007), domain-specific WSD - (Buitelaar et al., 2007) and as well discusses topics like automatic acquisition of lexical information (Gonzalo and Verdejo, 2007) and the application of WSD in Natural Language Processing (Resnik, 2007). The predicament that we aim to discuss in the following thesis is a yet open question in WSD. It is the quantity and quality of the automatically extracted data for the process of word sense disambiguation - we will approach questions like how much and what kind of data can be acquired without human labor. In our work we present a WSD system and its application to various types of data, which outlines an environment and basis for evaluation of the automatic acquisition of examples for word sense disambiguation. We will not pursuit an exhaustive em- pirical analysis of the latter but rather discuss the most relevant for our approach advantages and disadvantages of its employment. Since we do not concentrate on a particular WSD approach, but rather compare the results from several such in order to examine them in contrast we consider it extremely important first to examine the fundamentals of word sense disambiguation and all the basic approaches to it (Chapter 2). Chapter 3 and Chapter 4 are respectively concentrated on the ways to compare and evaluate WSD systems in general depending on the used by the system approach. Before reviewing our work, however, in Chapter 5 we will shortly introduce the software which we employed. The following chapter (Chapter 6) will discuss the structure of the suggested by us system and Chapter 7 will give a brief overview of the research that has already been done in that area and the work which will be attempted further on together with our concluding remarks.

Chapter 2 Basic Approaches to Word Sense Disambiguation Word sense disambiguation is the process of identification of the correct sense of a word in the context in which it is used. WSD has mainly one aim and it can be described in many analogous ways - to find out if two different words or phrases refer to the same entity, to be able to replace one with the other at the right place, to find out a sense that is common for both etc. In the pro- cess, however, there is an innumerable amount of unanswered questions and unsolved problems. One such question that turns out to be considerably essential for finding the right sense is the adequacy of the predefined set of senses a word can have (also called sense inventory - see Section 2.3.1). Are there too many distinctions between the senses? Are there too few? All such problems thus lead to many distinct approaches in the attempt to find the correct solutions. In the fol- lowing chapter we will give a short overview of the fundamental divisions in those attempts. In order to explain what kind of WSD technique we use in our work it will be very helpful to first have a look at what kind of approaches to the problem exist according to the current state of art. A very clear overview on the basic approaches in WSD has already been given by Agirre and Ed- monds (2007). The authors differ between knowledge-based (dictionary-based) and corpus-based (further divided as unsupervised, supervised and semi-supervised) approaches. In addition to that they discuss combinations of the different approaches which finally results in a variety which they summarize in a very precise manner as in Table 2.1 on page 13. 2.1 Knowledge-Based Knowledge-based Word Sense Disambiguation is build upon knowledge acquired from sources other than corpora (e.g. lexical knowledge bases, dictionaries, thesauri). 12

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