Multi-component Word Sense Disambiguation

Multi-Component Word Sense Disambiguation � Massimiliano Ciaramita Mark Johnson Brown University Department of Cognitive and Linguistic Sciences Providence, RI 02912 � massi@brown.edu,mark johnson@brown.edu � Abstract This paper describes the system MC-WSD pre- sented for the English Lexical Sample task. The system is based on a multicomponent architecture. It consists of one classifier with two components. One is trained on the data provided for the task. The second is trained on this data and, additionally, on an external training set extracted from the Wordnet glosses. The goal of the additional component is to lessen sparse data problems by exploiting the infor- mation encoded in the ontology. 1 Introduction One of the main difficulties in word sense classifi- cation tasks stems from the fact that word senses, such as Wordnet’s synsets (Fellbaum, 1998), de- fine very specific classes1. As a consequence train- ing instances are often too few in number to cap- ture extremely fine-grained semantic distinctions. Word senses, however, are not just independent enti- ties but are connected by several semantic relations; e.g., the is-a, which specifies a relation of inclusion among classes such as “car is-a vehicle”. Based on the is-a relation Wordnet defines large and complex hierarchies for nouns and verbs. These hierarchical structures encode potentially useful world-knowledge that can be exploited for word sense classification purposes, by providing means for generalizing beyond the narrowest synset level. To disambiguate an instance of a noun like “bat” a system might be more successful if, in- stead of limiting itself to applying what it knows about the concepts “bat-mammal” and “bat-sport- implement”, it could use additional knowledge about other “animals” and “artifacts”. Our system implements this intuition in two steps. First, for each sense of an ambiguous word we generate an additional set of training instances � We would like to thank Thomas Hofmann and our colleagues in the Brown Laboratory for Linguistic Information Processing (BLLIP). 151% of the noun synsets in Wordnet contain only 1 word. from the Wordnet glosses. This data is not limited to the specific synset that represents one of the senses of the word, but concerns also other synsets that are semantically similar, i.e., close in the hierarchy, to that synset. Then, we integrate the task-specific and the external training data with a multicomponent classifier that simplifies the system for hierarchical word sense disambiguation presented in (Ciaramita et al., 2003). The classifier consists of two com- ponents based on the averaged multiclass percep- tron (Collins, 2002; Crammer and Singer, 2003). The first component is trained on the task-specific data while the second is trained on the former and on the external training data. When predicting a la- bel for an instance the classifier combines the pre- dictions of the two components. Cross-validation experiments on the training data show the advan- tages of the multicomponent architecture. In the following section we describe the features used by our system. In Section 3 we explain how we generated the additional training set. In Section 4 we describe the architecture …

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