Computational Linguistics and Intelligent Text Processing: by Kevin Knight, Jonathan Graehl (auth.), Alexander Gelbukh

By Kevin Knight, Jonathan Graehl (auth.), Alexander Gelbukh (eds.)

CICLing 2005 ( was once the sixth Annual convention on clever textual content Processing and Computational Linguistics. It used to be meant to supply a balanced view of the state of the art advancements in either the theoretical foundations of computational linguistics and the perform of natural-language textual content processing with its a number of purposes. A function of CICLing meetings is their extensive scope that covers approximately all components of computational linguistics and all elements of traditional language processing purposes. This 12 months we have been commemorated through the presence of our keynote audio system Christian Boitet (CLIPS-IMAG, Grenoble), Kevin Knight (ISI), Daniel Marcu (ISI), and Ellen Riloff (University of Utah), who introduced first-class prolonged lectures and arranged shiny discussions and inspiring tutorials; their invited papers are released during this quantity. Of 151 submissions acquired, 88 have been chosen for presentation; fifty three as complete papers and 35 as brief papers, by means of precisely 2 hundred authors from 26 nations: united states (15 papers); Mexico (12); China (9.5); Spain (7.5); South Korea (5.5); Singapore (5); Germany (4.8); Japan (4); united kingdom (3.5); France (3.3); India (3); Italy (3); Czech Republic (2.5); Romania (2.3); Brazil, Canada, Greece, eire, Israel, the Netherlands, Norway, Portugal, Sweden, Switzerland (1 each); Hong Kong (0.5); and Russia (0.5) together with the invited papers. across the world co-authored papers are counted in equivalent fractions.

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Extra info for Computational Linguistics and Intelligent Text Processing: 6th International Conference, CICLing 2005, Mexico City, Mexico, February 13-19, 2005. Proceedings

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What is the most efficient algorithm for intersecting probabilistic RTGs? 6. What are the most efficient algorithms for forward and backward application of tree/tree and tree/string transducers? 7. For large tree transducers, what data structures, indexing strategies, and caching techniques will support efficient algorithms? 8. What is the linguistically most appropriate tree transducer class for machine translation? For summarization? Which classes best handle the most common linguistic constructions, and which classes best handle the most difficult ones?

The analysis is informed by Modelling Grammatical and Lexical Knowledge: A Declarative Approach 41 the assumption that syntactic expressions are endocentric. In other words, syntactic expressions are considered to be projections of lexical heads (so far, functional heads are not taken into account). The X-bar convention is adopted to represent syntactic configurations. 1 Secondary Predication As illustrated above, secondary predication involves an argument of the primary predicate – the external argument in (5a); the internal argument in (5b) – and a non-verbal predicate (for the sake of simplicity of the explanation only APs are considered in this paper, but PPs and AdvPs can also be secondary predicates) which expresses an atomic event (a state, in other words) that occurs in the same temporal interval in which occurs the primary event.

Addison-Wesley Series in Computer Science. Addison-Wesley, London (1979) 33. : PCFG models of linguistic tree representations. Computational Linguistics 24 (1998) 613–632 34. : Three generative, lexicalised models for statistical parsing. In: Proc. ACL. (1997) 35. : Characterizing derivation trees of context-free grammars through a generalization of finite automata theory. J. Comput. Syst. Sci. 1 (1967) 317–322 36. : Fundamental properties of pushdown tree transducers (PDTT) — a top-down case. IEICE Trans.

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