Titlebook: Natural Language Processing – IJCNLP 2004; First International Keh-Yih Su,Jun’ichi Tsujii,Oi Yee Kwong Conference proceedings 2005 Springe

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Combining Labeled and Unlabeled Data for Learning Cross-Document Structural Relationshipsucture Theory (CST), this paper describes an empirical study that classifies CST relationships between sentence pairs extracted from topically related documents, exploiting both labeled and unlabeled data. We investigate a binary classifier for determining existence of structural relationships and a

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Parsing Mixed Constructions in a Type Feature Structure Grammars computational analyses. Various theoretical approaches have been proposed to solve this puzzle, but they all have ended up abandoning or modifying fundamental theory-neutral desiderata such as endocentricity (every phrase has a head), lexicalism (no syntactic rule refers to the word-internal struc

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Iterative CKY Parsing for Probabilistic Context-Free Grammarsedges produced during parsing, which results in more efficient parsing. Since pruning is done by using the edge’s inside Viterbi probability and the upper-bound of the outside Viterbi probability, this algorithm guarantees to output the exact Viterbi parse, unlike beam-search or best-first strategie

debris

Causal Relation Extraction Using Cue Phrase and Lexical Pair Probabilitiesmade use of causal patterns such as causal verbs. We concentrate on the information obtained from other causal event pairs. If two event pairs share some lexical pairs and one of them is revealed to be causally related, the causal probability of another event pair tends to increase. We introduce the

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Negligible

OGLE

Chinese Named Entity Recognition Based on Multilevel Linguistic Featureshe advantages of character-based and word-based models. From experiments on a large-scale corpus, we show that significant performance enhancements can be obtained by integrating various linguistic information (such as Chinese word segmentation, semantic types, part of speech, and named entity trigg

FLING

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