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Combining dependency information and generalization in a pattern-based approach to the classification of lexical-semantic relation instances

This work addresses the classification of word pairs as instances of lexical-semantic relations. The classification is approached by leveraging patterns of co-occurrence contexts from corpus data. The significance of using dependency information, of augmenting the set of dependency paths provided to the system, and of generalizing patterns using part-of-speech information for the classification of lexical-semantic relation instances is analyzed. Results show that dependency information is decisive to achieve better results both in precision and recall, while generalizing features based on dependency information by replacing lexical forms with their part-of-speech increases the coverage of classification systems. Our experiments also make apparent that approaches based on the context where word pairs co-occur are upper-bound-limited by the times these appear in the same sentence. Therefore strategies to use information across sentence boundaries are necessary.