While automatic metrics of translation quality are invaluable for machine translation research, deeper understanding of translation errors require more focused evaluations designed to target specific aspects of translation quality. We show that Word Sense Disambiguation (WSD) can be used to evaluate the quality of machine translation lexical choice, by applying a standard phrase-based SMT syste...

An edge-labeling λ for a directed graph G has a weak sense of direction (WSD) if there is a function f that satisfies the condition that for any node u and for any two label sequences α and α′ generated by non-trivial walks on G starting at u, f(α) = f(α′) if and only if the two walks end at the same node. The function f is referred to as a coding function of λ. The weak sense of direction numb...

The word sense disambiguation (WSD) is the task ofautomatically selecting the correct sense given a context and it helps in solving many ambiguity problems inherently existing in all natural languages.Statistical Natural Language Processing (NLP),which is based on probabilistic, stochastic and statistical methods, has been used to solve many NLP problems.The Naive Bayes algorithm which is one o...

For many decades researchers in the domain of NLP (Natural Language Processing) and its applications like Machine Translation, Text Mining, Question Answering, Information Extraction and Information retrieval etc. have been posed with a challenging area of research i.e. WSD (Word Sense Disambiguation) WSD can be defined as the ability to correctly ascertain the meaning of a word, with reference...

This paper describes the automatic generation and the evaluation of sets of rules for word sense disambiguation (WSD) in machine translation. The ultimate aim is to identify high-quality rules that can be used as knowledge sources in a relational WSD model. The evaluation was carried out both automatically, by means of four objective measures (error, coverage, support and novelty), and manually...

While automatic metrics of translation quality are invaluable for machine translation research, deeper understanding of translation errors require more focused evaluations designed to target specific aspects of translation quality. We show that Word Sense Disambiguation (WSD) can be used to evaluate the quality of machine translation lexical choice, by applying a standard phrase-based SMT syste...

In this paper, we present an applicationoriented evaluation of three Part-ofSpeech (PoS) taggers in a word sense disambiguation (WSD) system. Following the intuition that high quality input is likely to influence the final results of a complex system, we test whether the more accurate taggers also produce better results when integrated into the WSD system. For this purpose, a stand-alone evalua...

Natural Languages used by people for establishing proper communication consist of many words having multiple meanings known as polysemous but implies a single sense depending on the context. Word sense disambiguation is a method of determining the appropriate sense of a polysemous word in the context. WSD is almost finished for English. It is a challenging task for Indian languages since these ...

We report on a series of experiments aimed at improving the machine translation of ambiguous lexical items by using wordnet-based unsupervised Word Sense Disambiguation (WSD) and comparing its results to three MT systems. Our experiments are performed for the English-Slovene language pair using UKB, a freely available graph-based word sense disambiguation system. Results are evaluated in three ...

Word sense disambiguation (WSD) is one of the most challenging outstanding problems in the current machine translation systems. An effective proposal in this context will rely on the use relevant knowledge sources. Moreover, it must perform better than the current traditional approaches. We present some experiments with machine learning algorithms traditionally applied to WSD, aiming to discove...