Advances in natural language processing
Advances in Natural Language Processing: 4th International Conference, EsTAL 2004, Alicante, Spain, October 20-22, 2004. Proceedings<br />Author: José Luis Vicedo, Patricio Martínez-Barco, Rafael Muńoz, Maximiliano Saiz Noeda<br /> Published by Springer Berlin Heidelberg<br /> ISBN: 978-3-540-23498-2<br /> DOI: 10.1007/b101638<br /><br />Table of Contents:<p></p><ul><li>Adaptive Selection of Base Classifiers in One-Against-All Learning for Large Multi-labeled Collections </li><li>Automatic Acquisition of Transfer Rules from Translation Examples </li><li>Automatic Assessment of Open Ended Questions with a Bleu-Inspired Algorithm and Shallow NLP </li><li>Automatic Phonetic Alignment and Its Confidence Measures </li><li>Automatic Spelling Correction in Galician </li><li>Baseline Methods for Automatic Disambiguation of Abbreviations in Jewish Law Documents </li><li>Bayes Decision Rules and Confidence Measures for Statistical Machine Translation </li><li>Character Identification in Children Stories </li><li>Comparison and Evaluation of Two Approaches of a Multilayered QA System Applied to Temporality </li><li>The Contents and Structure of the Context Base, and Its Application </li><li>Developing a Minimalist Parser for Free Word Order Languages with Discontinuous Constituency </li><li>Developing Competitive HMM PoS Taggers Using Small Training Corpora </li><li>Exploring the Use of Target-Language Information to Train the Part-of-Speech Tagger of Machine Translation Systems </li><li>Expressive Power and Consistency Properties of State-of-the-Art Natural Language Parsers </li><li>An Independent Domain Dialogue System Through a Service Manager </li><li>Information Retrieval in Digital Theses Based on Natural Language Processing Tools </li><li>Integrating Conceptual Density with WordNet Domains and CALD Glosses for Noun Sense Disambiguation </li><li>Intertwining Deep Syntactic Processing and Named Entity Detection </li><li>Language Understanding Using n-multigram Models </li><li>Multi-label Text Classification Using Multinomial Models</li></ul>