Este trabajo ha sido parcialmente subvencionado por los proyectos CICYT TIC2000-0664-C02-01 y TIC2000-1599-C01-01.

In this work, we propose a supervised approach to Word Sense Disambiguation which is based on Specialized Hidden Markov Models and the use of WordNet. Our approach formulates the disambiguation process as a tagging problem. The specialization process allows for the incorporation of additional knowledge into the models. We evaluated our system on the English all-words taskof the Senseval-2 competition. The performance of our system is in line with the best approaches for this task.

Una formulación unificada para resolver distintos problemas de ambigüedad en PLN

A Hidden Markov Model Approach to Word Sense Disambiguation

We present a supervised approach to Word Sense Disambiguation (WSD) based on Specialized Hidden Markov Models. We used as training data the Semcor corpus and the test data set provided by Senseval 2 competition and as dictionary the Wordnet 1.6. We evaluated our system on the English all-word task of the Senseval-3 competition. 1 Description of the WSD System We consider WSD to be a tagging problem (Molina et al., 2002a). The tagging process can be formulated as a maximization problem using the Hidden Markov Model (HMM) formalism. Let O be the set of output tags considered, and I , the input vocabulary of the application. Given an input sentence, I = i1, . . . , iT , where ij ∈ I , the tagging process consists of finding the sequence of tags (O = o1, . . . , oT , where oj ∈ O) of maximum probability on the model, that is: Ô = arg max O P (O|I) = arg max O ( P (O) · P (I|O)

methods

WSD system based on specialized Hidden Markov Model (upv-shmm-eaw)

/pdf/una-formulacion-unificada-para-resolver-distintos-problemas-325tmdqkbr.pdf

Una formulación unificada para resolver distintos problemas de ambigüedad en PLN

Citations

A Hidden Markov Model Approach to Word Sense Disambiguation

WSD system based on specialized Hidden Markov Model (upv-shmm-eaw)