tailieunhanh - Báo cáo khoa học: "ixtending the intity qrid with intity"

Extend the popul r entity grid represent E tion for lo l oheren e modelingF he grid str ts w y inform tion out the entities it modelsY we dd dis ourse prominen eD n med entity type nd oreferen e fe tures to distinE guish etween import nt nd unimport nt enE titiesF e improve the est result for WSJ do E ument dis rimin models @ ori ut nd w r uD PHHTY ilsner nd gh rE ni kD PHHVAD whi h typi lly om ine it with other independentlyEtr ined modelsF here h ve een few ttempts to improve the enE tity grid dire tly y ltering its fe ture represent E tionF pilippov nd tru e @PHHUA in orpor te seE m nti rel tednessD ut ¢nd no signi¢ nt improveE 125 Proceedings of. | Extending the Entity Grid with Entity-Specific Features Micha Elsner School of Informatics University of Edinburgh Eugene Chamiak Department of Computer Science Brown University Providence RI 02912 ec@ Abstract We extend the popular entity grid representation for local coherence modeling. The grid abstracts away information about the entities it models we add discourse prominence named entity type and coreference features to distinguish between important and unimportant entities. We improve the best result for WSJ document discrimination by 6 . 1 Introduction A well-written document is coherent Halliday and Hasan 1976 - it structures information so that each new piece of information is interpretable given the preceding context. Models that distinguish coherent from incoherent documents are widely used in generation summarization and text evaluation. Among the most popular models of coherence is the entity grid Barzilay and Lapata 2008 a statistical model based on Centering Theory Grosz et al. 1995 . The grid models the way texts focus on important entities assigning them repeatedly to prominent syntactic roles. While the grid has been successful in a variety of applications it is still a surprisingly unsophisticated model and there have been few direct improvements to its simple feature set. We present an extension to the entity grid which distinguishes between different types of entity resulting in significant gains in performance1. At its core the grid model works by predicting whether an entity will appear in the next sentence a public implementation is available via https melsner browncoherence. 125 and what syntactic role it will have given its history of occuưences in the previous sentences. For instance it estimates the probability that Clinton will be the subject of sentence 2 given that it was the subject of sentence 1. The standard grid model uses no information about the entity itself- the probability is the .