The University of Alicante Word Sense Disambiguation System

The University of Alicante Word Sense Disambiguation System* Andres Montoya and Armando Suarez Departamento de Lenguajes y Sistemas Informaticos Universidad de Alicante Alicante, Spain {montoyo I armando}@dlsi.ua.es Abstract The WSD system presented at SENSEVAL-2 uses a knowledge-based method for noun dis- ambiguation and a corpus-based method for verbs and adjectives. The methods are, respec- tively, Specification Marks and Maximum En- tropy probability models. So, we can say that this is a hybrid system which joins an unsuper- vised method with a supervised method. The whole system has been used in lexical sample english task and lexical sample spanish task. 1 Introduction In this paper a Word Sense Disambiguation sys- tem based on Specification Marks (SM) and Maximum Entropy probability models {ME) is presented. SM is an unsupervised knowledge- based method and has been applied to noun disambiguation. ME belongs to the statistical approach to WSD in NLP and uses a tagged cor- pus in order to learn a probability model that can be used to predict the correct sense of a word. SM does not need a previously tagged corpus, it uses the semantic information stored in WordNet. The weakness of supervised corpus-based ap- proaches rely on availability of corpora and their dependency of the data which were used in the training phase. Knowledge-based approaches use previously acquire linguistic knowledge. This knowledge is extracted from human lex- icographers experience and can be in form of electronic dictionary or lexicon. While their success seems poorest than statistical methods, they don't need neither an existing corpus nor a training phase and they can be more domain independent. * This paper has been partially supported by the Span- ish Government (CICYT) project number TIC2000- 0664-C02-02. 131 So, the University of Alicante system per- forms the WSD task combining unsupervised with supervised methods. The whole system has been used in lexical sample English task and lexical sample Spanish task. 2 Specification Marks Framework The method we present here consists basically of the automatic sense-disambiguating of nouns that appear within the context of a sentence and whose different possible senses are quite re- lated. Its context is the group of words that co-occur with it in the sentence and their rela- tionship to the noun to be disambiguated. The disambiguation is resolved with the use of the WordNet lexical knowledge base. The intuition underlying this approach is that the more similar two words are, the more infor- mative the most specific concept that subsumes them both will be. In other words, their low- est upper bound in the taxonomy. (A "con- cept" here, corresponds to a Specification Mark (SM)). In other words, the more information two concepts share in common, the more similar they obviously are, and the information com- monly shared by two concepts is indicated by the concept that subsumes them in the taxon- omy. The input for the WSD module will be the group of words W = {W1, W2, ... , Wn}· Each word wi is sought in WordNet, each one …

Content truncated to 500 words