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.

In this paper we describe the English Lexical Substitution task for SemEval. In the task, annotators and systems find an alternative substitute word or phrase for a target word in context. The task involves both finding the synonyms and disambiguating the context. Participating systems are free to use any lexical resource. There is a subtask which requires identifying cases where the word is functioning as part of a multiword in the sentence and detecting what that multiword is. 1

Since the inception of the SENSEVAL series there has been a great deal of debate in the word sense disambiguation (WSD) community on what the right sense distinctions are for evaluation, with the consensus of opinion being that the distinctions should be relevant to the intended application. A solution to the above issue is lexical substitution, i.e. the replacement of a target word in context with a suitable alternative substitute. In this paper, we describe the English lexical substitution task and report an exhaustive evaluation of the systems participating in the task organized at SemEval-2007. The aim of this task is to provide an evaluation where the sense inventory is not predefined and where performance on the task would bode well for applications. The task not only reflects WSD capabilities, but also can be used to compare lexical resources, whether man-made or automatically created, and has the potential to benefit several natural-language applications.

The work described in this paper was originally motivated by the need to map verbs associated with FrameNet 1.2 frames to appropriate WordNet 2.0 senses. As the work evolved, it became apparent that the developed method was applicable for a number of other tasks, including assignment of WordNet senses to word lists used in attitude and opinion analysis, and collapsing WordNet senses into coarser-grained groupings. We describe the method for mapping FrameNet lexical units to WordNet senses and demonstrate its applicability to these additional tasks. We conclude with a general discussion of the viability of using this method with automatically sense-tagged data.

Word sense disambiguation is typically phrased as the task of labeling a word in context with the best-fitting sense from a sense inventory such as WordNet. While questions have often been raised over the choice of sense inventory, computational linguists have readily accepted the bestfitting sense methodology despite the fact that the case for discrete sense boundaries is widely disputed by lexical semantics researchers. This paper studies graded word sense assignment, based on a recent dataset of graded word sense annotation. 1

We present a pilot study of word-sense annotation using multiple annotators, relatively polysemous words, and a heterogenous corpus. Annotators selected senses for words in context, using an annotation interface that presented WordNet senses. Interannotator agreement (IA) results show that annotators agree well or not, depending primarily on the individual words and their general usage properties. Our focus is on identifying systematic differences across words and annotators that can account for IA variation. We identify three lexical use factors: semantic specificity of the context, sense concreteness, and similarity of senses. We discuss systematic differences in sense selection across annotators, and present the use of association rules to mine the data for systematic differences across annotators. 1

The vast majority of work on word senses has relied on predefined sense inventories and an annotation schema where each word instance is tagged with the best fitting sense. This paper examines the case for a graded notion of word meaning in two experiments, one which uses WordNet senses in a graded fashion, contrasted with the “winner takes all ” annotation, and one which asks annotators to judge the similarity of two usages. We find that the graded responses correlate with annotations from previous datasets, but sense assignments are used in a way that weakens the case for clear cut sense boundaries. The responses from both experiments correlate with the overlap of paraphrases from the English lexical substitution task which bodes well for the use of substitutes as a proxy for word sense. This paper also provides two novel datasets which can be used for evaluating computational systems. 1

Manual annotation of natural language to capture linguistic information is essential for NLP tasks involving supervised machine learning of semantic knowledge. Judgements of meaning can be more or less subjective, in which case instead of a single correct label, the labels assigned might vary among annotators based on the annotators ’ knowledge, age, gender, intuitions, background, and so on. We introduce a framework ”Anveshan, ” where we investigate annotator behavior to find outliers, cluster annotators by behavior, and identify confusable labels. We also investigate the effectiveness of using trained annotators versus a larger number of untrained annotators on a word sense annotation task. The annotation data comes from a word sense disambiguation task for polysemous words, annotated by both trained annotators and untrained annotators from Amazon’s Mechanical turk. Our results show that Anveshan is effective in uncovering patterns in annotator behavior, and we also show that trained annotators are superior to a larger number of untrained annotators for this task.

Supervised word sense disambiguation requires training corpora that have been tagged with word senses, which begs the question of which word senses to tag with. The default choice has been WordNet, with its broad coverage and easy accessibility. However, concerns have been raised about the appropriateness of its fine-grained word senses for WSD. WSD systems have been far more successful in distinguishing coarsegrained senses than fine-grained ones (Navigli, 2006), but does that approach neglect necessary meaning differences? Recent psycholinguistic evidence seems to indicate

In this paper, we present a unified model for the automatic induction of word senses from text, and the subsequent disambiguation of particular word instances using the automatically extracted sense inventory. The induction step and the disambiguation step are based on the same principle: words and contexts are mapped to a limited number of topical dimensions in a latent semantic word space. The intuition is that a particular sense is associated with a particular topic, so that different senses can be discriminated through their association with particular topical dimensions; in a similar vein, a particular instance of a word can be disambiguated by determining its most important topical dimensions. The model is evaluated on the SEMEVAL-2010 word sense induction and disambiguation task, on which it reaches stateof-the-art results. 1