Most word representation methods assume that each word owns a single semantic vector. This is usually problematic because lexical ambiguity is ubiquitous, which is also the problem to be resolved by word sense disambiguation. In this paper, we present a unified model for joint word sense representation and disambiguation, which will assign distinct representations for each word sense.1 The basi...

Natural language processing (NLP) has become the most significant obstacle that has been restricting the applications via the web. Today, very little of the content on the web can be understood by the machines, although vast amount of electronic information has been kept on them. Word sense disambiguation (WSD) is an important intermediate step in many language processing applications. It is ba...

This paper describes a new approach to word sense disambiguation (WSD) based on automatically acquired "word sense division. The semantically related sense entries in a bilingual dictionary are arranged in clusters using a heuristic labeling algorithm to provide a more complete and appropriate sense division for WSD. Multiple translations of senses serve as outside information for automatic tag...

Implementations of word sense disambiguation (WSD) algorithms tend to be tied to a particular test corpus format and sense inventory. This makes it difficult to test their performance on new data sets, or to compare them against past algorithms implemented for different data sets. In this paper we present DKPro WSD, a freely licensed, general-purpose framework for WSD which is both modular and ...

White Spot Disease (WSD) is well known as a widespread viral disease in shrimps from 1992. Many studies focused on morphological, histopathological and epidemiological characteristic and pathogenecity of the disease but less on the determination of the severity of WSD using the histopathological features in target tissues. A generalized scheme for assigning a numerical qualitative value to sev...

  The first time the white spot disease (WSD) and IHHNV were reported in Iran was in 2004 and 2008, respectively in Bushehr Province. In Sistan and Balochestan province only the WSD was reported in 2008.The aim of this study was detecting these two viral diseases in these provinces, during December 2009 to April 2010. A total of 364 samples were collected according to suspected gross signs from...

Evaluation of word sense disambiguation (WSD) systems is often based on machine-readable dictionaries (MRDs). Such evaluation typically employs a set of fine-grained dictionary senses and considers them all to be equally important. In this paper, we propose a novel evaluation method for WSD systems in the context of automatic subcategorization acquisition. Building on an extant subcategorizatio...

The task of Word Sense Disambiguation (WSD) incorporates in its definition the role of ‘context’. We present our work on the development of a tool which allows for automatic acquisition and ranking of ‘context clues’ for WSD. These clue words are extracted from the contexts of words appearing in a large monolingual corpus. These mined collection of contextual clues form a discrimination net in ...

Word Sense Disambiguation (WSD) aims at identifying the correct sense of a word in a given context. WSD is an important task for other applications as Machine Translation or Information Retrieval. For English, WSD has been widely studied, obtaining different performances. Analyzing by morphosyntactic class, Verb is the hardest class to be disambiguated. Verbs are an important class and help to ...

An acid test for any new Word Sense Disambiguation (WSD) algorithm is its performance against the Most Frequent Sense (MFS). The field of WSD has found the MFS baseline very hard to beat. Clearly, if WSD researchers had access to MFS values, their striving to better this heuristic will push the WSD frontier. However, getting MFS values requires sense annotated corpus in enormous amounts, which ...