We describe experiments in Machine Translation using word sense disambiguation (WSD) information. This work focuses on WSD in verbs, based on two different approaches – verbal patterns based on corpus pattern analysis and verbal word senses from valency frames. We evaluate several options of using verb senses in the source-language sentences as an additional factor for the Moses statistical mac...

The application of Word Sense Disambiguation (WSD) is usually determined exclusively by the trust in the disambiguation system used. In this paper, a study in the Information Retrieval (IR) field is carried out about the impact of others factors in WSD such as the confidence of the WSD tool, the grade of polisemy or granularity and the difference in the discrimination strength between the origi...

This work models Word Sense Disambiguation (WSD) problem as a Distributed Constraint Optimization Problem (DCOP). To model WSD as a DCOP, we view information from various knowledge sources as constraints. DCOP algorithms have the remarkable property to jointly maximize over a wide range of utility functions associated with these constraints. We show how utility functions can be designed for var...

In computational linguistics, word sense disambiguation (WSD) is the problem of determining in which sense a word having a number of distinct senses is used in a given sentence . This paper handles text document clustering as one of the major tasks of text processing. Document clustering is the process of finding out groups of information from the text documents and cluster these documents into...

State of the art Word Sense Disambiguation (WSD) systems require large sense-tagged corpora along with lexical databases to reach satisfactory results. The number of English language resources for developed WSD increased in the past years, while most other languages are still under-resourced. The situation is no different for Dutch. In order to overcome this data bottleneck, the DutchSemCor pro...

Corpus-based techniques have proved to be very beneficial in the development of efficient and accurate approaches to word sense disambiguation (WSD) despite the fact that they generally represent relatively shallow knowledge. It has always been thought, however, that WSD could also benefit from deeper knowledge sources. We describe a novel approach to WSD using inductive logic programming to le...

Word Sense Disambiguation (WSD) is one of the fundamental natural language processing tasks. However, lack of training corpora is a bottleneck to construct a high accurate all-words WSD system. Annotating a large-scale corpus by experts costs enormous time and financial resources. Human Computation is a novel idea for integrating human resources behind the Web, which has been wasted, to solve p...

Word Sense Disambiguation (WSD) systems are usually evaluated by comparing their absolute performance, in a fixed experimental setting, to other alternative algorithms and methods. However, little attention has been paid to analyze the lexical resources and the corpora defining the experimental settings and their possible interactions with the overall results obtained. In this paper we present ...

Toxicity tests were conducted simulating a diesel oil spill in a tropical environment and juveniles of Prochilodus lineatus were exposed to the water-soluble fraction of diesel oil (WSD) for 6, 24, 96 h, and 15 days. The results showed the activation of biotransformation pathways for xenobiotics, through a time-dependent increase of liver GST activity. WSD caused a decrease in hematocrit and he...

Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we present an unsupervised bootstrapping approach for WSD which exploits huge amounts of automatically generated noisy data for training within a supervised learning framework. The method is evaluated using...