Word sense disambiguation: a survey <s> 2.A BRIEF HISTORY OF RESEARCH ON WORD SENSE DISAMBIGUATION <s> Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought. <s> BIB001 </s> Word sense disambiguation: a survey <s> 2.A BRIEF HISTORY OF RESEARCH ON WORD SENSE DISAMBIGUATION <s> This invention describes a system and method for precisely locating a combined sleeve bolt in an opening located in a plurality of materials adapted to be connected together. The adjustable nut is constructed in two parts and rotatably attached to each other. The first part contains an internal thread for accepting one end of the sleeve bolt and a bearing surface for bearing only on one end of the projecting outer sleeve. The second portion of the nut has a greater diameter for bearing against the bottommost portion of the materials adapted to be connected together. The two portions of the nut being independently adjustable allow the sleeve bolt to be positioned within the hole regardless of any variations in the length of the sleeve. Once the desired position is achieved, the two portions are locked together, preferably by a set screw, and the tapered bolt is pulled into the hole while the adjustable nut holds the sleeve in the desired and pre-adjusted position. <s> BIB002 </s> Word sense disambiguation: a survey <s> 2.A BRIEF HISTORY OF RESEARCH ON WORD SENSE DISAMBIGUATION <s> The construction of a concept map consists of enumerating a list of concepts and -a more difficult task- determining the linking phrases that should connect the concepts to form meaningful propositions. Appropriate word selection, both for concepts and linking phrases, is key for an accurate knowledge representation of the user's understanding of the domain. We present an algorithm that uses WordNet to disambiguate the sense of a word from a concept map, using the map itself to provide its context. Results of preliminary experimental evaluations of the algorithm are presented. We propose to use the algorithm to (a) enhance the understanding of the concept map by modules in the CmapTools software that aide the user during map construction, and (b) sort the meanings of a word selected from a concept map according to their relevance within the map when the user navigates through WordNet's hierarchies searching for more appropriate terms. <s> BIB003 </s> Word sense disambiguation: a survey <s> 2.A BRIEF HISTORY OF RESEARCH ON WORD SENSE DISAMBIGUATION <s> In this paper, we propose an automatic text classification method based on word sense disambiguation. We use "hood" algorithm to remove the word ambiguity so that each word is replaced by its sense in the context. The nearest ancestors of the senses of all the non-stopwords in a give document are selected as the classes for the given document. We apply our algorithm to Brown Corpus. The effectiveness is evaluated by comparing the classification results with the classification results using manual disambiguation offered by Princeton University. <s> BIB004 </s> Word sense disambiguation: a survey <s> 2.A BRIEF HISTORY OF RESEARCH ON WORD SENSE DISAMBIGUATION <s> In this paper we present the methodology for word sense disambiguation based on domain information. Domain is a set of words in which there is a strong semantic relation among the words. The words in the sentence contribute to determine the domain of the sentence. The availability of WordNet domains makes the domain-oriented text analysis possible. The domain of the target word can be fixed based on the domains of the content words in the local context. This approach can be effectively used to disambiguate nouns. We present the unsupervised approach to word sense disambiguation using the WordNet domains. The model determines the domain of the target word and the sense corresponding to this domain is taken as the correct sense. We have used the WordNet domains 3.1 as lexical database. <s> BIB005
WSD is one of the most challenging jobs in the research field of Natural Language Processing. Research work  in this domain was started during the late 1940s. In 1949, Zipf proposed his "Law of Meaning" theory. This theory states that there exists a power-law relationship between the more frequent words and the less frequent words. The more frequent words have more senses than the less frequent words. The relationship has been confirmed later for the British National Corpus. In 1950, Kaplan determined that in a particular context two words on either side of an ambiguous word are equivalent to the whole sentence of the context. In 1957, Masterman proposed his theory of finding the actual sense of a word using the headings of the categories present in Roget's International Thesaurus. In 1975 Wilks developed a model on "preference semantics", where the selectional restrictions and a frame-based lexical semantics were used to find the exact sense of an ambiguous word. Rieger and Small in 1979 evolved the idea of individual "word experts". In 1980s there was a remarkable development in the field of WSD research as Large-scale lexical resources and corpora became available during this time. As a result, researchers started using different automatic knowledge extraction procedures (Wilks et al. 1990 ) parallel with the handcrafting methodologies. In 1986, Lesk proposed his algorithm based on overlaps between the glosses (Dictionary definitions) of the words in a sentence. The maximum number of overlaps represents the desired sense of the ambiguous word. In this approach the Oxford Advanced Learner's Dictionary of Current English (OALD) was used to obtain the dictionary definitions. This approach had shown the way to the other Dictionary-based WSD works. In 1991, Guthrie et al. used the subject codes to disambiguate the exact sense using the Longman Dictionary of Contemporary English (LDOCE). In 1990s, three major developments occurred in the research fields of NLP: online dictionary WordNet  BIB001 BIB005 BIB004 BIB001 BIB002 BIB003 became available, the statistical methodologies were introduced in this domain, and Senseval began. The invention of WordNet BIB001 ) brought a revolution in this research field because it was both programmatically accessible and hierarchically organized into word senses called synsets. Today, WordNet is used as an important online sense inventory in WSD research. Statistical and machine learning methods are also successfully used in the sense classification problems. Today, methods that are trained on manually sense-tagged corpora (i.e., supervised learning methods) have become the mainstream approach to WSD. Corpus based Word Sense Disambiguation was first implemented by Brown et al. in 1991. As the data sets, corpuses, online Dictionaries vary language to language all over the world, there was not any bench mark of performance measurement in this domain in the early age. Senseval brought all kind of research works in this domain under a single umbrella. The first Senseval was proposed in 1997 by Resnik andYarowsky. Now, after hosting the three Senseval evaluation exercises, all over the world researchers can share and upgrade their views in this research field.