text: In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm is inspired by a widely-used approach in the field of genetics for whole genome sequencing, known as the Shotgun sequencing technique. The proposed WSD algorithm is based on three main steps. First, a brute-force WSD algorithm is applied to short context windows (up to 10 words) selected from the document in order to generate a short list of likely sense configurations for each window. In the second step, these local sense configurations are assembled into longer composite configurations based on suffix and prefix matching. The resulted configurations are ranked by their length, and the sense of each word is chosen based on a voting scheme that considers only the top k configurations in which the word appears. We compare our algorithm with other state-of-the-art unsupervised WSD algorithms and demonstrate better performance, sometimes by a very large margin. We also show that our algorithm can yield better performance than the Most Common Sense (MCS) baseline on one data set. Moreover, our algorithm has a very small number of parameters, is robust to parameter tuning, and, unlike other bio-inspired methods, it gives a deterministic solution (it does not involve random choices).
  @cite_18: The problem and process of identifying the meaning of a word as per its usage context is called word sense disambiguation (WSD). Although research in this field has been ongoing for the past forty years, a distinct change of techniques adopted can be observed over time. Two important parameters govern the direction in which WSD research progresses during any period. These are the underlying requirement of the kind of sense disambiguation, or the domain, and the robustness of available knowledge in the form of corpora or dictionaries. This paper surveys the progress of WSD over time and the important linguistic achievements that enabled this progress.
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  @cite_22: Word sense disambiguation (WSD) is a difficult problem in Computational Linguistics, mostly because of the use of a fixed sense inventory and the deep level of granularity. This paper formulates WSD as a variant of the traveling salesman problem (TSP) to maximize the overall semantic relatedness of the context to be disambiguated. Ant colony optimization, a robust nature-inspired algorithm, was used in a reinforcement learning manner to solve the formulated TSP. We propose a novel measure based on the Lesk algorithm and Vector Space Model to calculate semantic relatedness. Our approach to WSD is comparable to state-of-the-art knowledge-based and unsupervised methods for benchmark datasets. In addition, we show that the combination of knowledge-based methods is superior to the most frequent sense heuristic and significantly reduces the difference between knowledge-based and supervised methods. The proposed approach could be customized for other lexical disambiguation tasks, such as Lexical Substitution or Word Domain Disambiguation.
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  @cite_36: Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet 1 provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].
  @cite_0: This is the first book to cover the entire topic of word sense disambiguation (WSD) including: all the major algorithms, techniques, performance measures, results, philosophical issues, and applications. Leading researchers in the field have contributed chapters that synthesize and provide an overview of past and state-of-the-art research across the field. The editors have carefully organized the chapters into sub-topics. Researchers and lecturers will learn about the full range of what has been done and where the field is headed. Developers will learn which technique(s) will apply to their particular application, how to build and evaluate systems, and what performance to expect. An accompanying Website provides links to resources for WSD and a searchable index of the book.
  @cite_19: This paper presents an adaptation of Lesk's dictionary-based word sense disambiguation algorithm. Rather than using a standard dictionary as the source of glosses for our approach, the lexical database WordNet is employed. This provides a rich hierarchy of semantic relations that our algorithm can exploit. This method is evaluated using the English lexical sample data from the SENSEVAL-2 word sense disambiguation exercise, and attains an overall accuracy of 32 . This represents a significant improvement over the 16 and 23 accuracy attained by variations of the Lesk algorithm used as benchmarks during the Senseval-2 comparative exercise among word sense disambiguation systems.
  @cite_24: This paper presents a new measure of semantic relatedness between concepts that is based on the number of shared words (overlaps) in their definitions (glosses). This measure is unique in that it extends the glosses of the concepts under consideration to include the glosses of other concepts to which they are related according to a given concept hierarchy. We show that this new measure reasonably correlates to human judgments. We introduce a new method of word sense disambiguation based on extended gloss overlaps, and demonstrate that it fares well on the SENSEVAL-2 lexical sample data.
  @cite_2: 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.
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  @cite_15: Recent years have seen a dramatic growth in the popularity of word embeddings mainly owing to their ability to capture semantic information from massive amounts of textual content. As a result, many tasks in Natural Language Processing have tried to take advantage of the potential of these distributional models. In this work, we study how word embeddings can be used in Word Sense Disambiguation, one of the oldest tasks in Natural Language Processing and Artificial Intelligence. We propose different methods through which word embeddings can be leveraged in a state-of-the-art supervised WSD system architecture, and perform a deep analysis of how different parameters affect performance. We show how a WSD system that makes use of word embeddings alone, if designed properly, can provide significant performance improvement over a state-ofthe-art WSD system that incorporates several standard WSD features.
  @cite_13: Part 1 The lexical database: nouns in WordNet, George A. Miller modifiers in WordNet, Katherine J. Miller a semantic network of English verbs, Christiane Fellbaum design and implementation of the WordNet lexical database and searching software, Randee I. Tengi. Part 2: automated discovery of WordNet relations, Marti A. Hearst representing verb alterations in WordNet, Karen T. the formalization of WordNet by methods of relational concept analysis, Uta E. Priss. Part 3 Applications of WordNet: building semantic concordances, Shari performance and confidence in a semantic annotation task, Christiane WordNet and class-based probabilities, Philip Resnik combining local context and WordNet similarity for word sense identification, Claudia Leacock and Martin Chodorow using WordNet for text retrieval, Ellen M. Voorhees lexical chains as representations of context for the detection and correction of malapropisms, Graeme Hirst and David St-Onge temporal indexing through lexical chaining, Reem Al-Halimi and Rick Kazman COLOR-X - using knowledge from WordNet for conceptual modelling, J.F.M. Burg and R.P. van de Riet knowledge processing on an extended WordNet, Sanda M. Harabagiu and Dan I Moldovan appendix - obtaining and using WordNet.
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summary: There is a broad range of methods designed to perform WSD @cite_0 @cite_7 @cite_18 . The most accurate techniques are supervised @cite_15 , but they require annotated training data which is not always available. In order to overcome this limitation, some researchers have proposed various unsupervised or knowledgde-based WSD methods @cite_19 @cite_24 @cite_5 @cite_22 @cite_26 @cite_37 @cite_17 @cite_2 @cite_25 . Since our approach is unsupervised and based on WordNet @cite_36 @cite_13 , our focus is to present related work in the same direction. extend the gloss overlap algorithm of by using WordNet relations. proposed a brute-force algorithm for global WSD by employing the extended Lesk measure @cite_19 @cite_24 to compute the semantic relatedness among senses in a given text. However, their BF approach is not suitable for whole text documents, because of the high computational time. More recently, have proposed and compared three stochastic algorithms for global WSD as alternatives to BF search, namely a Genetic Algorithm, Simulated Annealing, and Ant Colony Optimization. Among these, the authors @cite_5 @cite_26 have found that the Ant Colony Algorithm yields better results than the other two.