Learning Word-Class Lattices for Definition and Hypernym Extraction

Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1318–1327, Uppsala, Sweden, 11-16 July 2010. c⃝2010 Association for Computational Linguistics Learning Word-Class Lattices for Definition and Hypernym Extraction Roberto Navigli and Paola Velardi Dipartimento di Informatica Sapienza Universit`a di Roma {navigli,velardi}@di.uniroma1.it Abstract Definition extraction is the task of au- tomatically identifying definitional sen- tences within texts. The task has proven useful in many research areas including ontology learning, relation extraction and question answering. However, current ap- proaches – mostly focused on lexico- syntactic patterns – suffer from both low recall and precision, as definitional sen- tences occur in highly variable syntactic structures. In this paper, we propose Word- Class Lattices (WCLs), a generalization of word lattices that we use to model tex- tual definitions. Lattices are learned from a dataset of definitions from Wikipedia. Our method is applied to the task of def- inition and hypernym extraction and com- pares favorably to other pattern general- ization methods proposed in the literature. 1 Introduction Textual definitions constitute a fundamental source to look up when the meaning of a term is sought. Definitions are usually collected in dictio- naries and domain glossaries for consultation pur- poses. However, manually constructing and up- dating glossaries requires the cooperative effort of a team of domain experts. Further, in the presence of new words or usages, and – even worse – new domains, such resources are of no help. Nonethe- less, terms are attested in texts and some (usually few) of the sentences in which a term occurs are typically definitional, that is they provide a formal explanation for the term of interest. While it is not feasible to manually search texts for definitions, this task can be automatized by means of Machine Learning (ML) and Natural Language Processing (NLP) techniques. Automatic definition extraction is useful not only in the construction of glossaries, but also in many other NLP tasks. In ontology learning, definitions are used to create and enrich concepts with textual information (Gangemi et al., 2003), and extract taxonomic and non-taxonomic rela- tions (Snow et al., 2004; Navigli and Velardi, 2006; Navigli, 2009a). Definitions are also har- vested in Question Answering to deal with “what is” questions (Cui et al., 2007; Saggion, 2004). In eLearning, they are used to help students as- similate knowledge (Westerhout and Monachesi, 2007), etc. Much of the current literature focuses on the use of lexico-syntactic patterns, inspired by Hearst’s (1992) seminal work. However, these methods suffer both from low recall and precision, as defi- nitional sentences occur in highly variable syntac- tic structures, and because the most frequent def- initional pattern – X is a Y – is inherently very noisy. In this paper we propose a generalized form of word lattices, called Word-Class Lattices (WCLs), as an alternative to lexico-syntactic pattern learn- ing. A lattice is a directed acyclic graph (DAG), a subclass of non-deterministic finite state automata (NFA). The lattice structure has the purpose of preserving the salient differences among distinct sequences, while …

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