Structural semantic interconnection: a knowledge-based approach to Word Sense Disambiguation

Structural Semantic Interconnection: a knowledge-based approach to Word Sense Disambiguation Roberto NAVIGLI Dipartimento di Informatica, Università di Roma “La Sapienza” Via Salaria, 113 - 00198 Roma, Italy navigli@di.uniroma1.it Paola VELARDI Dipartimento di Informatica, Università di Roma “La Sapienza” Via Salaria, 113 - 00198 Roma, Italy velardi@di.uniroma1.it Abstract In this paper we describe the SSI algorithm, a structural pattern matching algorithm for WSD. The algorithm has been applied to the gloss disambiguation task of Senseval-3. 1 Introduction Our approach to WSD lies in the structural pattern recognition framework. Structural or syntactic pattern recognition (Bunke and Sanfeliu, 1990) has proven to be effective when the objects to be classified contain an inherent, identifiable organization, such as image data and time-series data. For these objects, a representation based on a “flat” vector of features causes a loss of information that negatively impacts on classification performances. Word senses clearly fall under the category of objects that are better described through a set of structured features. The classification task in a structural pattern recognition system is implemented through the use of grammars that embody precise criteria to discriminate among different classes. Learning a structure for the objects to be classified is often a major problem in many application areas of structural pattern recognition. In the field of computational linguistics, however, several efforts have been made in the past years to produce large lexical knowledge bases and annotated resources, offering an ideal starting point for constructing structured representations of word senses. 2 Building structural representations of word senses We build a structural representation of word senses using a variety of knowledge sources, i.e. WordNet, Domain Labels (Magnini and Cavaglia, 2000), annotated corpora like SemCor and LDC- DSO1. We use this information to automatically 1 LDC http://www.ldc.upenn.edu/ generate labeled directed graphs (digraphs) representations of word senses. We call these semantic graphs, since they represent alternative conceptualizations for a lexical item. Figure 1 shows an example of the semantic graph generated for senses #1 of market, where nodes represent concepts (WordNet synsets), and edges are semantic relations. In each graph, we include only nodes with a maximum distance of 3 from the central node, as suggested by the dashed oval in Figure 1. This distance has been experimentally established. market#1 goods#1 trading#1 gloss gloss merchandise #1 kind-of monopoly#1 kind-of export#1 has-kind activity#1 has-kind consumer goods#1 grocery#2 kind-of kind-of load#3 kind-of commercial enterprise#2 has-part commerce#1 kind-of transportation#5 has-part business activity#1 gloss service#1 gloss topic industry#2 kind-of has-p art gloss kind-of food#1 clothing#1 gloss gloss enterprise#1 kind-of production#1 artifact#1 kind-of express#1 kind-of consumption#1 gloss Figure 1. Graph representations for sense #1 of market. All the used semantic relations are explicitly encoded in WordNet, except for three relations named topic, gloss and domain, extracted respectively from annotated corpora, sense definitions and domain labels. 3 Summary description of the SSI algorithm The SSI algorithm consists of an initialization step and an iterative step. In a generic iteration of the algorithm the input is a list of co-occurring terms T = [ t1, …

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