Automatic Verbalisation of Biological Events

Bikash Gyawali 1 Claire Gardent 1 Christophe Cerisara 1
1 SYNALP - Natural Language Processing : representations, inference and semantics
LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : We present a method for automatically generating descriptions of biological events encoded in the KB BIO 101 Knowledge base. In this knowledge base, events are concepts (e.g., RELEASE) related by role relations (e.g., AGENT, PATIENT, PATH, INSTRUMENT) to the concepts denoting their arguments (e.g., GATED-CHANNEL, VASCULAR-TISSUE, IRON). We propose a probabilistic, unsupervised method which extracts possible verbalisation frames from large biology specific domain corpora and uses probabilities both to select an appropriate frame given an event description and to determine the mapping between syntactic and semantic arguments. That is, probabilities are used to determine which event argument fills which syntactic function (e.g., subject, object) in the produced verbalisation. We evaluate our approach on a corpus of 336 event descriptions, provide a qualitative and quantitative analysis of the results obtained and discuss possible directions for further work.
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Bikash Gyawali, Claire Gardent, Christophe Cerisara. Automatic Verbalisation of Biological Events. International Workshop on Definitions in Ontologies (IWOOD 2015), Jul 2015, Lisbon, Portugal. 2015. 〈hal-01214569〉

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