acl acl2010 acl2010-260 knowledge-graph by maker-knowledge-mining

260 acl-2010-Wide-Coverage NLP with Linguistically Expressive Grammars


Source: pdf

Author: Julia Hockenmaier ; Yusuke Miyao ; Josef van Genabith

Abstract: unkown-abstract

Reference: text


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 Wide-coverage NLP with Linguistically Expressive Grammars Julia Hockenmaier Department of Computer Science, University of Illinois j uliahmr@ i l l inoi s . [sent-1, score-0.1]

2 edu Yusuke Miyao National Institute of Informatics yusuke @ nii ac . [sent-2, score-0.3]

3 Josef van Genabith Centre for Next Generation Localisation, School of Computing, Dublin City University j o s e f@ comput ing . [sent-4, score-0.155]

4 But although many young researchers in natural language processing are very well trained in machine learning and statistical methods, they often lack the necessary background to understand the linguistic motivation behind these formalisms. [sent-7, score-0.375]

5 Furthermore, in many linguistics departments, syntax is still taught from a purely Chomskian perspective. [sent-8, score-0.148]

6 Additionally, research on these formalisms often takes place within tightly-knit, formalism- specific subcommunities. [sent-9, score-0.407]

7 It is therefore often difficult for outsiders as well as experts to grasp the commonalities of and differences between these formalisms. [sent-10, score-0.286]

8 2 Content Overview This tutorial overviews basic ideas of TAG/ CCG/LFG/HPSG, and provides attendees with a comparison of these formalisms from a linguistic and computational point of view. [sent-11, score-0.749]

9 We start from stating the motivation behind using these expressive grammar formalisms for NLP, contrasting them with shallow formalisms like contextfree grammars. [sent-12, score-1.523]

10 In the 1 second half of the tutorial, we explain two key technologies for wide-coverage NLP with these grammar formalisms: grammar acquisition and parsing models. [sent-14, score-0.322]

11 Finally, we show NLP applications where these expressive grammar formalisms provide additional benefits. [sent-15, score-0.796]

12 Summary References parsing with expressive Aoife Cahill, Michael Burke, Ruth O’Donovan, Stefan Riezler, Josef van Genabith and Andy Way. [sent-25, score-0.486]


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wordName wordTfidf (topN-words)

[('expressive', 0.372), ('formalisms', 0.338), ('grammars', 0.316), ('yusuke', 0.208), ('hpsg', 0.196), ('ccg', 0.191), ('genabith', 0.163), ('lfg', 0.149), ('miyao', 0.144), ('tutorial', 0.143), ('hockenmaier', 0.135), ('julia', 0.127), ('grasp', 0.1), ('taught', 0.1), ('inoi', 0.1), ('overviews', 0.092), ('nii', 0.092), ('attendees', 0.092), ('scrambling', 0.092), ('aoife', 0.092), ('burke', 0.092), ('donovan', 0.092), ('departments', 0.092), ('josef', 0.089), ('reuse', 0.086), ('dcu', 0.086), ('comput', 0.086), ('localisation', 0.086), ('cahill', 0.086), ('grammar', 0.086), ('linguistically', 0.082), ('ccgbank', 0.082), ('asscolcia', 0.082), ('cgeom', 0.082), ('jtuulytor', 0.082), ('dublin', 0.082), ('commonalities', 0.082), ('ruth', 0.078), ('nlp', 0.078), ('motivation', 0.076), ('pra', 0.075), ('behind', 0.072), ('stating', 0.072), ('contrasting', 0.072), ('illustrating', 0.069), ('experts', 0.069), ('mit', 0.069), ('van', 0.069), ('illinois', 0.067), ('unbounded', 0.067), ('sweden', 0.067), ('riezler', 0.065), ('uppsala', 0.065), ('combinatory', 0.064), ('andy', 0.062), ('coordination', 0.06), ('contextfree', 0.06), ('ichi', 0.059), ('af', 0.059), ('bounded', 0.058), ('categorial', 0.058), ('tag', 0.056), ('outline', 0.055), ('forest', 0.055), ('centre', 0.055), ('jp', 0.054), ('sc', 0.054), ('informatics', 0.054), ('constructions', 0.051), ('stefan', 0.05), ('formalism', 0.05), ('arise', 0.049), ('young', 0.049), ('purely', 0.048), ('jun', 0.048), ('ideas', 0.046), ('inducing', 0.045), ('mappings', 0.045), ('parsing', 0.045), ('lot', 0.045), ('deep', 0.044), ('derivations', 0.043), ('introducing', 0.042), ('city', 0.042), ('dependency', 0.038), ('linguistic', 0.038), ('explain', 0.037), ('understand', 0.037), ('shallow', 0.037), ('half', 0.037), ('school', 0.036), ('fo', 0.036), ('often', 0.035), ('background', 0.035), ('place', 0.034), ('researchers', 0.033), ('summary', 0.033), ('additionally', 0.032), ('penn', 0.032), ('acquisition', 0.031)]

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