VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations

VERBOCEAN: Mining the Web for Fine-Grained Semantic Verb Relations Timothy Chklovski and Patrick Pantel Information Sciences Institute University of Southern California 4676 Admiralty Way Marina del Rey, CA 90292 {timc, pantel}@isi.edu Abstract Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs using lexico- syntactic patterns over the Web. On a set of 29,165 strongly associated verb pairs, our extraction algo- rithm yielded 65.5% accuracy. Analysis of error types shows that on the relation strength we achieved 75% accuracy. We provide the resource, called VERBOCEAN, for download at http://semantics.isi.edu/ocean/. 1 Introduction Many NLP tasks, such as question answering, summarization, and machine translation could benefit from broad-coverage semantic resources such as WordNet (Miller 1990) and EVCA (Eng- lish Verb Classes and Alternations) (Levin 1993). These extremely useful resources have very high precision entries but have important limitations when used in real-world NLP tasks due to their limited coverage and prescriptive nature (i.e. they do not include semantic relations that are plausible but not guaranteed). For example, it may be valu- able to know that if someone has bought an item, they may sell it at a later time. WordNet does not include the relation “X buys Y” happens-before “X sells Y” since it is possible to sell something with- out having bought it (e.g. having manufactured or stolen it). Verbs are the primary vehicle for describing events and expressing relations between entities. Hence, verb semantics could help in many natural language processing (NLP) tasks that deal with events or relations between entities. For tasks which require canonicalization of natural language statements or derivation of plausible inferences from such statements, a particularly valuable re- source is one which (i) relates verbs to one another and (ii) provides broad coverage of the verbs in the target language. In this paper, we present an algorithm that semi- automatically discovers fine-grained verb seman- tics by querying the Web using simple lexico- syntactic patterns. The verb relations we discover are similarity, strength, antonymy, enablement, and temporal relations. Identifying these relations over 29,165 verb pairs results in a broad-coverage re- source we call VERBOCEAN. Our approach extends previously formulated ones that use surface pat- terns as indicators of semantic relations between nouns (Hearst 1992; Etzioni 2003; Ravichandran and Hovy 2002). We extend these approaches in two ways: (i) our patterns indicate verb conjuga- tion to increase their expressiveness and specificity and (ii) we use a measure similar to mutual infor- mation to account for both the frequency of the verbs whose semantic relations are being discov- ered as well as for the frequency of the pattern. 2 Relevant Work In this section, we describe application domains that can benefit from a resource of verb semantics. We then introduce some existing resources and describe previous attempts at mining semantics from text. 2.1 Applications Question answering is often approached …

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