Srinivas Bangalore

Aravind Krishna Joshi

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 hand-crafted natural language grammars in formal frameworks such as Tree-Adjoining Grammar (TAG), Combinatory Categorical Grammar (CCG), Head-driven 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 parse-annotated 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 Tree-Adjoining Grammar (LTAG). Contributors Jens Bäcker, 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, Günter Neumann, Martha Palmer, Owen Rambow, Rajeev Sangal, Anoop Sarkar, Giorgio Satta, Libin Shen, Patrick Sturt, Jun'ichi Tsujii, K. Vijay-Shanker, Wen Wang, Fei Xia

Using Complex Lexical Descriptions in Natural Language Processing

Supertagging

Michael Carl

Moritz Schaeffer

This volume provides a comprehensive introduction to the Translation Process Research Database (TPR-DB), which was compiled by the Centre for Research and Innovation in Translation and Technologies (CRITT). The TPR-DB is a unique resource featuring more than 500 hours of recorded translation process data, augmented with over 200 different rich annotations. Twelve chapters describe the diverse research directions this data can support, including the computational, statistical and psycholinguistic modeling of human translation processes. In the first chapters of this book, the reader is introduced to the CRITT TPR-DB. This is followed by two main parts, the first of which focuses on usability issues and details of implementing interactive machine translation. It also discusses the use of external resources and translator-information interaction. The second part addresses the cognitive and statistical modeling of human translation processes, including co-activation at the lexical, syntactic and discourse levels, translation literality, and various annotation schemata for the data.

Exploring the CRITT TPR-DB

New Directions in Empirical Translation Process Research

Amanda Stent

A comprehensive overview of the state-of-the-art in natural language generation for interactive systems, with links to resources for further research.

Srinivas Bangalore

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