Alexander Gelbukh Computational Linguistics and Intelligent Text Processing 9783642372469

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Автор: Alexander Gelbukh
Название: Computational Linguistics and Intelligent Text Processing
ISBN: 9783642372469
Издательство: Springer
Классификация: ISBN-10: 3642372465
Обложка/Формат: Paperback
Страницы: 576
Вес: 0.83 кг.
Дата издания: 19.03.2013
Серия: Theoretical Computer Science and General Issues
Язык: English
Размер: 234 x 156 x 31
Основная тема: Computer Science
Подзаголовок: 14th International Conference, CICLing 2013, Samos, Greece, March 24-30, 2013, Proceedings, Part I
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: General Techniques.- Unsupervised Feature Adaptation for Cross-Domain NLP with an Application to Compositionality Grading.- Syntactic Dependency-Based N-grams: More Evidence of Usefulness in Classification.- Lexical Resources.- A Quick Tour of BabelNet 1.1 (Invited paper).- Automatic Pipeline Construction for Real-Time Annotation.- A Multilingual GRUG Treebank for Underresourced Languages.- Creating an Annotated Corpus for Extracting Canonical Citations from Classics-Related Texts by Using Active Annotation.- Approaches of Anonymisation of an SMS Corpus.- A Corpus Based Approach for the Automatic Creation of Arabic Broken Plural Dictionaries.- Temporal Classifiers for Predicting the Expansion of Medical Subject Headings.- Knowledge Discovery on Incompatibility of Medical Concepts.- Extraction of Part-Whole Relations from Turkish Corpora.- Chinese Terminology Extraction Using EM-Based Transfer Learning Method.- Orthographic Transcription for Spoken Tunisian Arabic.- Morphology and Tokenization.- An Improved Stemming Approach Using HMM for a Highly Inflectional Language.- Semi-automatic Acquisition of Two-Level Morphological Rules for Iban Language.- Finite State Morphology for Amazigh Language.- New Perspectives in Sinographic Language Processing through the Use of Character Structure.- The Application of Kalman Filter Based Human-Computer Learning Model to Chinese Word Segmentation.- Machine Learning for High-Quality Tokenization Replicating Variable Tokenization Schemes.- Syntax and Named Entity Recognition Structural Prediction in Incremental Dependency Parsing.- Semi-supervised Constituent Grammar Induction Based on Text Chunking Information.- Turkish Constituent Chunking with Morphological and Contextual Features.- Enhancing Czech Parsing with Verb Valency Frames.- An Automatic Approach to Treebank Error Detection Using a Dependency Parser.- Topic-Oriented Words as Features for Named Entity Recognition (Best Paper Award, Second Place).- Named Entities in Judicial Transcriptions: Extended Conditional Random Fields.- Introducing Baselines for Russian Named Entity Recognition.- Word Sense Disambiguation and Coreference Resolution Five Languages Are Better Than One: An Attempt to Bypass the Data Acquisition Bottleneck for WSD (Best Paper Award, Third Place).- Analyzing the Sense Distribution of Concordances Obtained by Web as Corpus Approach.- MaxMax: A Graph-Based Soft Clustering Algorithm Applied to Word Sense Induction.- A Model of Word Similarity Based on Structural Alignment of Subject-Verb-Object Triples.- Coreference Annotation Schema for an Inflectional Language.- Exploring Coreference Uncertainty of Generically Extracted Event Mentions.- Semantics and Discourse.- LIARc: Labeling Implicit ARguments in Spanish Deverbal Nominalizations.- Automatic Detection of Idiomatic Clauses (Best Paper Award, First Place).- Evaluating the Results of Methods for Computing Semantic Relatedness.- Similarity Measures Based on Latent Dirichlet Allocation.- Evaluating the Premises and Results of Four Metaphor Identification Systems.- Determining the Conceptual Space of Metaphoric Expressions.- What is being Measured in an Information Graphic? .- Comparing Discourse Tree Structures.- Assessment of Different Workflow Strategies for Annotating Discourse Relations: A Case Study with HDRB.- Building a Discourse Parser for Informal Mathematical Discourse in the Context of a Controlled Natural Language.- Discriminative Learning of First-Order Weighted Abduction from Partial Discourse Explanations.- Facilitating the Analysis of Discourse Phenomena in an Interoperable NLP Platform.

Computational Linguistics and Intelligent Text Processing, Alexander Gelbukh