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 ...

Word Sense Disambiguation (WSD) is considered as one of the toughest problem in the field of Natural Language Processing. IndoWordNet is a linked structure of WordNets of major Indian languages. Recently, several IndoWordNet based WSD approaches have been proposed and implemented for Indian languages. In this chapter, we present the usage of various other features of IndoWordNet in performing W...

Word Sense Disambiguation (WSD) systems tend to have a strong bias towards assigning the Most Frequent Sense (MFS), which results in high performance on the MFS but in a very low performance on the less frequent senses. We addressed the MFS bias in WSD systems by combining the output from a WSD system with a set of mostly static features to create a MFS classifier to decide when to and not to c...

Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning. However, the published literature on WSD systems for biomedical documents report considerable differences in performance for different terms. The development of...

Although it is generally agreed that Word Sense Disambiguation (WSD) is an application dependent task, the great majority of the efforts has aimed at the development of WSD systems without considering their application. We argue that this strategy is not appropriate, since some aspects, such as the sense repository and the disambiguation process itself, vary according to the application. Taking...