Titlebook: Natural Language Processing and Information Systems; 10th International C Andrés Montoyo,Rafael Muńoz,Elisabeth Métais Conference proceedin

Adentitious

書目名稱Natural Language Processing and Information Systems影響因子(影響力)




書目名稱Natural Language Processing and Information Systems影響因子(影響力)學(xué)科排名




書目名稱Natural Language Processing and Information Systems網(wǎng)絡(luò)公開度




書目名稱Natural Language Processing and Information Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Natural Language Processing and Information Systems被引頻次




書目名稱Natural Language Processing and Information Systems被引頻次學(xué)科排名




書目名稱Natural Language Processing and Information Systems年度引用




書目名稱Natural Language Processing and Information Systems年度引用學(xué)科排名




書目名稱Natural Language Processing and Information Systems讀者反饋




書目名稱Natural Language Processing and Information Systems讀者反饋學(xué)科排名

不幸的人

https://doi.org/10.1007/b136569SQL; algorithms; bioinformatics; classification; common; computational linguistics; filtering; information

胰臟

978-3-540-26031-8Springer-Verlag Berlin Heidelberg 2005

arousal

Natural Language Processing and Information Systems978-3-540-32110-1Series ISSN 0302-9743 Series E-ISSN 1611-3349

Awning

善辯

Extended Tagging and Interpretation Tools for Mapping Requirements Texts to Conceptual (Predesign) Mrequirements specification to a conceptual (predesign) model.The focus lies on sentences containing conditions as used for describing alternatives in business process specifications. It is shown, how the tagging results are interpreted systematically such allowing for a stepwise model generation.

occult

被告

Using Semantic Roles in Information Retrieval SystemsInformation Retrieval Systems the incorporation of Natural Language Processing techniques that provide semantic information is needed. For this reason, in this paper a method to determine the semantic role for the constituents of a sentence is presented. The goal of this is to integrate this method in an Information Retrieval System.

Pcos971

Combining Data-Driven Systems for Improving Named Entity Recognitionare trained on the same corpus data and after comparison their outputs are combined using voting strategy. Results are encouraging since 98.5% accuracy for recognition and 84.94% accuracy for classification of NE for Spanish language were achieved.

EVICT

Improving Text Categorization Using Domain Knowledgeis very useful for improving text categorization. BOTW model performs better than BOW model, and BOL and BOF models perform better than BOW model in small number of features cases. Through learning new features using machine learning technique, BOL model performs better than BOF model.