Automated Verb Sense Identification

"This task seeks to develop an automated, domain-portable module for verb sense identification (VSI) in natural language processing (NLP). Since verbs are pivot carriers of event concepts, VSI, a primary sub-task of Word Sense Disambiguation (WSD), hassignificant impact on Information Extraction (IE). Currently, only keyword-based IE, mainly identification of named entities and simple, general events, is available. Concept-based extraction of events requires support from WSD.A scalable VSI system will extend InfoXtract, an IE system equipped with machine learning and grammar development toolkits. Language-specific, key-verb-based representation of extracted events will be translated into an `interlingua' based on actionconcepts.Previous systems for sense disambiguation are limited to research prototypes relying on human-annotated corpora. The proposed approach is based on unsupervised machine learning from an InfoXtract-parsed corpus. Decision list models will be trained on anInfoXtract-parsed corpus from which a domain-dependent, WordNet-like thesaurus is constructed. Bootstrap training, using initial seeds, is performed on a new corpus without human intervention by leveraging the core IE engine.Deliverables include: (i) a VSI system demonstrating domain-specific machine learning and application of the learned model; (ii) integrating VSI into InfoXtract to demonstrate added value in event extraction and retrieval in political