tailieunhanh - Báo cáo khoa học: "Event Extraction as Dependency Parsing"

Nested event structures are a common occurrence in both open domain and domain specific extraction tasks, ., a “crime” event can cause a “investigation” event, which can lead to an “arrest” event. However, most current approaches address event extraction with highly local models that extract each event and argument independently. | Event Extraction as Dependency Parsing David McClosky Mihai Surdeanu and Christopher D. Manning Department of Computer Science Stanford University Stanford CA 94305 mcclosky mihais manning @ Abstract Nested event structures are a common occurrence in both open domain and domain specific extraction tasks . a crime event can cause a investigation event which can lead to an arrest event. However most current approaches address event extraction with highly local models that extract each event and argument independently. We propose a simple approach for the extraction of such structures by taking the tree of event-argument relations and using it directly as the representation in a reranking dependency parser. This provides a simple framework that captures global properties of both nested and flat event structures. We explore a rich feature space that models both the events to be parsed and context from the original supporting text. Our approach obtains competitive results in the extraction of biomedical events from the BioNLP 09 shared task with a F1 score of in development and in testing. 1 Introduction Event structures in open domain texts are frequently highly complex and nested a crime event can cause an investigation event which can lead to an arrest event Chambers and Jurafsky 2009 . The same observation holds in specific domains. For example the BioNLP 09 shared task Kim et al. 2009 focuses on the extraction of nested biomolecular events where . a REGULATION event causes a TRANSCRIPTION event see Figure 1a for a detailed example . Despite this observation many state-of-the-art supervised event extraction models still 1626 extract events and event arguments independently ignoring their underlying structure Bjorne et al. 2009 Miwa et al. 2010b . In this paper we propose a new approach for supervised event extraction where we take the tree of relations and their arguments and use it directly as the representation in a dependency parser .