UBB system at Senseval-3

UBB system at Senseval3 Gabriela Serban Department of Computer Science University ”Babes-Bolyai” Romania gabis@cs.ubbcluj.ro Doina Tatar Department of Computer Science University ”Babes-Bolyai” Romania dtatar@cs.ubbcluj.ro Abstract It is known that whenever a system’s actions depend on the meaning of the text being pro- cessed, disambiguation is beneficial or even nec- essary. The contest Senseval is an international frame where the research in this important field is validated in an hierarchical manner. In this paper we present our system participating for the first time at Senseval 3 contest on WSD, contest developed in March-April 2004. We present also our intentions on improving our system, intentions occurred from the study of results. 1 Introduction Word Sense Disambiguation (WSD) is the pro- cess of identifying the correct meanings of words in particular contexts (Manning and Schutze, 1999). It is only an intermediate task in NLP, like POS tagging or parsing. Examples of final tasks are Machine Translation, Information Ex- traction or Dialogue systems. WSD has been a research area in NLP for almost the beginning of this field due to the phenomenon of polysemy that means multiple related meanings with a single word (Widdows, 2003). The most im- portant robust methods in WSD are: machine learning methods and dictionary based meth- ods. While for English exist some machine read- able dictionaries, the most known being Word- Net (Christiane Fellbaum, 1998), for Romanian until now does not exist any. Therefore for our application we used the machine learning ap- proach. 2 Machine learning approach in WSD Our system falls in the supervised learning ap- proach category. It was trained to learn a clas- sifier that can be used to assign a yet unseen ex- ample to one or two of a fixed number of senses. We had a trained corpus (a number of annotated contexts), from where the system learned the classifier, and a test corpus which the system will annotate. In our system we used the Vector Space Model: a context c was represented as a vec- tor ⃗c of some features which we will present bel- low. By a context we mean the same definition as in Senseval denotation: the content between ¡context¿ and ¡/context¿. The notations used to explain our method are (Manning and Schutze, 1999): • w - the word to be disambiguate; • s1, · · · , sNs the senses for w; • c1, · · · , cNc the contexts for w; • v1, · · · , vNf the features selected. As we treated each word w to be disam- biguated separately, let us explain the method for a single word. The features selected was the set of ALL words used in the trained corpus (nouns, verbs, prepositions, etc) , so we used the cooccurrence paradigm (Dagan, Lee and Pereira , 1994). The vector of a context c of the target word w is defined as: • ⃗c = (w1, · · · , w|W|) where wi is the number of occurences of the word vi in …

Content truncated to 500 words