The paper presents experimental results on WSD, with focus on disambiguation of Russian nouns that refer to tangible objects and abstract notions. The body of contexts has been extracted from the Russian National Corpus (RNC). The tool used in our experiments is aimed at statistical processing and classification of noun contexts. The WSD procedure takes into account taxonomy markers of word mea...

In this paper, we present a word sense disambiguation (WSD) based system for multilingual lexical substitution. Our method depends on having a WSD system for English and an automatic word alignment method. Crucially the approach relies on having parallel corpora. For Task 2 (Sinha et al., 2009) we apply a supervised WSD system to derive the English word senses. For Task 3 (Lefever & Hoste, 2009...

We develop a model for predicting verb sense from subcategorization information and integrate it into SSI-Dijkstra, a wide-coverage knowledge-based WSD algorithm. Adding syntactic knowledge in this way should correct the current poor performance of WSD systems on verbs. This paper also presents, for the first time, an evaluation of SSI-Dijkstra on a standard data set which enables a comparison ...

Clustering methods have been extensively used in many Information Processing tasks in order to capture unknown object categories. However, clustering has been scarcely used as a sense labeling method for Word Sense Disambiguation (WSD), that is, as a way to identify groups of semantically related word senses that can be successfully used in a disambiguation process. In this paper, we present an...

Current word sense disambiguation (WSD) systems based on supervised learning are still limited in that they do not work well for all words in a language. One of the main reasons is the lack of sufficient training data. In this paper, we investigate the use of unlabeled training data for WSD, in the framework of semi-supervised learning. Four semisupervised learning algorithms are evaluated on 2...

As text data becomes plentiful, unsupervised methods for Word Sense Disambiguation (WSD) become more viable. A problem encountered in applying WSD methods is finding the exact number of senses an ambiguity has in a training corpus collected in an automated manner. That number is not known a priori; rather it needs to be determined based on the data itself. We address that problem using cluster ...

INTRODUCTION In this article, we evaluate a knowledge-based word sense disambiguation method that determines the intended concept associated with an ambiguous word in biomedical text using semantic similarity and relatedness measures. These measures quantify the degree of similarity or relatedness between concepts in the Unified Medical Language System (UMLS). The objective of this work is to d...

This paper describes our participation in the Robust WSD Task within the CLEF 2008. The aim of this pilot task is exploring methods which can take profit of WSD information in order to improve the IR systems. In our approach we have used a passage based system jointly with a WordNet based expansion method for the collection documents and the queries using the two WSD systems runs provided by th...

Word Sense Disambiguation (WSD) is the task of computational assignment of correct sense of a polysemous word in a given context. This paper compares three WSD algorithms for Hindi WSD based on corpus statistics. The first algorithm, called corpus-based Lesk, uses sense definitions and a sense tagged training corpus to learn weights of Content Words (CWs). These weights are used in the disambig...

An open problem in natural language processing is word sense disambiguation (WSD). A word may have several meanings, but WSD is the task of selecting the correct sense of a polysemous word based on its context. Proposed solutions are based on supervised and unsupervised learning methods. The majority of researchers in the area focused on choosing proper size of ‘n’ in n-gram that is used for WS...