Titlebook: Computational Linguistics and Intelligent Text Processing; 12th International C Alexander F. Gelbukh Conference proceedings 2011 Springer B

Dislocation

The Creation of the World Assembly of Youth,, TTFs approximate linguistic notions such as grammatical weight, branching property and structural parallelism. This is illustrated by studying how the features capture structural parallelism in processing coordinate structures.

murmur

Combining Contextual and Structural Information for Supersense Tagging of Chinese Unknown Wordsual similarity between words while structural information is used to filter candidate synonyms and adjusting contextual similarity score. Experiment results show that the proposed approach outperforms the state-of-art context-based method and structure-based method.

EWER

Identification of Reduplicated Multiword Expressions Using CRF surrounding stem words, length of the word, word frequency and digit feature. Experimental results show the effectiveness of the proposed approach with the overall average Recall, Precision and F-Score values of 92.91%, 91.90% and 92.40% respectively.

TATE

CHAFE

拍翅

Dependency Syntax Analysis Using Grammar Induction and a Lexical Categories Precedence Systemmance of our parser, which needs no syntactic tagged resources or rules, trained with a small corpus, is 10% below to that of commercial semi-supervised dependency analyzers for Spanish, and comparable to the state of the art for English.

PLIC

decipher

An Analysis of Tree Topological Features in Classifier-Based Unlexicalized Parsing, TTFs approximate linguistic notions such as grammatical weight, branching property and structural parallelism. This is illustrated by studying how the features capture structural parallelism in processing coordinate structures.

露天历史剧

思考而得

Studentisches Publizieren: Wie? Wo? Warum?thm to convert CCGbank normal form derivations to incremental left-to-right derivations and show that our incremental CCG derivations can recover the unlabeled predicate-argument dependency structures with more than 96% F-measure. The introduced CCG incremental derivations can be used to train an incremental CCG parser.