Title
Supertagging: Using Complex Lexical Descriptions In Natural Language Processing (Bradford Books),Used
Delivery time: 8-12 business days (International)
Investigations into employing statistical approaches with linguistically motivated representations and its impact on Natural Language processing tasks.The last decade has seen computational implementations of large handcrafted natural language grammars in formal frameworks such as TreeAdjoining Grammar (TAG), Combinatory Categorical Grammar (CCG), Headdriven Phrase Structure Grammar (HPSG), and Lexical Functional Grammar (LFG). Grammars in these frameworks typically associate linguistically motivated rich descriptions (Supertags) with words. With the availability of parseannotated corpora, grammars in the TAG and CCG frameworks have also been automatically extracted while maintaining the linguistic relevance of the extracted Supertags. In these frameworks, Supertags are designed so that complex linguistic constraints are localized to operate within the domain of those descriptions. While this localization increases local ambiguity, the process of disambiguation (Supertagging) provides a unique way of combining linguistic and statistical information. This volume investigates the theme of employing statistical approaches with linguistically motivated representations and its impact on Natural Language Processing tasks. In particular, the contributors describe research in which words are associated with Supertags that are the primitives of different grammar formalisms including Lexicalized TreeAdjoining Grammar (LTAG).ContributorsJens Bcker, Srinivas Bangalore, Akshar Bharati, Pierre Boullier, Tomas By, John Chen, Stephen Clark, Berthold Crysmann, James R. Curran, Kilian Foth, Robert Frank, Karin Harbusch, Sasa Hasan, Aravind Joshi, Vincenzo Lombardo, Takuya Matsuzaki, Alessandro Mazzei, Wolfgang Menzel, Yusuke Miyao, Richard Moot, Alexis Nasr, Gnter Neumann, Martha Palmer, Owen Rambow, Rajeev Sangal, Anoop Sarkar, Giorgio Satta, Libin Shen, Patrick Sturt, Jun'ichi Tsujii, K. VijayShanker, Wen Wang, Fei Xia
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