Titlebook: Language Processing and Knowledge in the Web; 25th International C Iryna Gurevych,Chris Biemann,Torsten Zesch Conference proceedings 2013 S

运动性

Phrase Tagset Mapping for French and English Treebanks and Its Application in Machine Translation Es an obstacle for other researchers to take full advantages of them, especially when they undertake the multilingual research. To address this problem and to facilitate future research in unsupervised induction of syntactic structures, some researchers have developed a universal POS tag set. However

legacy

CEDE

CHASM

传授知识

Fine-Grained POS Tagging of German Tweets,ned analysis of POS tags on Twitter microtext is feasible. Our CRF-based tagger achieves an accuracy of around 89% when trained on LDA word clusters, features from an automatically created dictionary and additional out-of-domain training data.

COM

Data-Driven vs. Dictionary-Based Word ,-Gram Feature Induction for Sentiment Analysis,t analysis within a specific domain: a purely data-driven word .-gram feature induction or a word .-gram feature induction based on a domain-specific or domain-non-specific polarity dictionary. We evaluate both approaches in document-level polarity classification experiments in 2 languages, English

Amnesty

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0302-9743 International Conference on Language Processing and Knowledge in the Web, GSCL 2013, held in Darmstadt, Germany, in September 2013. The 20 revised full papers were carefully selected from numerous submissions and cover topics on language processing and knowledge in the Web on several important dimen

不可接触

A Joint Inference Architecture for Global Coreference Clustering with Anaphoricity,al consistency constraints. Defining pairwise coreference features in a global setting achieves an efficient entity-based perspective. With a small feature set we obtain a performance of 63.56% (gold mentions) on the official CoNLL 2012 data set.

Erythropoietin

,SdeWaC – A Corpus of Parsable Sentences from the Web, remaining textual material might be suitable for certain research requirements. This paper describes methods utilized to prepare ., a freely available German web corpus of the WaCky project, for automatic processing up to the parsing level. It then discusses ways in which this corpus, called SdeWaC, has been used since its release.