Natural Language Processing Using Very Large Corpora

✍ Scribed by H. Feldweg (auth.), Susan Armstrong, Kenneth Church, Pierre Isabelle, Sandra Manzi, Evelyne Tzoukermann, David Yarowsky (eds.)

• Publisher: Springer Netherlands
• Year: 1999
• Tongue: English
• Leaves: 314
• Series: Text, Speech and Language Technology 11
• Edition: 1
• Category: Library

✦ Synopsis

ABOUT THIS BOOK This book is intended for researchers who want to keep abreast of cur­ rent developments in corpus-based natural language processing. It is not meant as an introduction to this field; for readers who need one, several entry-level texts are available, including those of (Church and Mercer, 1993; Charniak, 1993; Jelinek, 1997). This book captures the essence of a series of highly successful work­ shops held in the last few years. The response in 1993 to the initial Workshop on Very Large Corpora (Columbus, Ohio) was so enthusias­ tic that we were encouraged to make it an annual event. The following year, we staged the Second Workshop on Very Large Corpora in Ky­ oto. As a way of managing these annual workshops, we then decided to register a special interest group called SIGDAT with the Association for Computational Linguistics. The demand for international forums on corpus-based NLP has been expanding so rapidly that in 1995 SIGDAT was led to organize not only the Third Workshop on Very Large Corpora (Cambridge, Mass. ) but also a complementary workshop entitled From Texts to Tags (Dublin). Obviously, the success of these workshops was in some measure a re­ flection of the growing popularity of corpus-based methods in the NLP community. But first and foremost, it was due to the fact that the work­ shops attracted so many high-quality papers.

✦ Table of Contents

Front Matter....Pages i-xvii
Implementation and Evaluation of a German HMM for POS Disambiguation....Pages 1-12
Improvements in Part-of-Speech Tagging with an Application to German....Pages 13-25
Unsupervised Learning of Disambiguation Rules for Part-of-Speech Tagging....Pages 27-42
Tagging French without Lexical Probabilities — Combining Linguistic Knowledge and Statistical Learning....Pages 43-65
Example-Based Sense Tagging of Running Chinese Text....Pages 67-75
Disambiguating Noun Groupings with Respect to WordNet Senses....Pages 77-98
A Comparison of Corpus-Based Techniques for Restoring Accents in Spanish and French Text....Pages 99-120
Beyond Word N -Grams....Pages 121-136
Statistical Augmentation of a Chinese Machine-Readable Dictionary....Pages 137-155
Text Chunking Using Transformation-Based Learning....Pages 157-176
Prepositional Phrase Attachment Through a Backed-off Model....Pages 177-189
On the Unsupervised Induction of Phrase-Structure Grammars....Pages 191-208
Robust Bilingual Word Alignment for Machine Aided Translation....Pages 209-224
Iterative Alignment of Syntactic Structures for a Bilingual Corpus....Pages 225-234
Trainable Coarse Bilingual Grammars for Parallel Text Bracketing....Pages 235-252
Comparative Discourse Analysis of Parallel Texts....Pages 253-268
Comparing the Retrieval Performance of English and Japanese Text Databases....Pages 269-282
Inverse Document Frequency (IDF): A Measure of Deviations from Poisson....Pages 283-295
Back Matter....Pages 297-305

✦ Subjects

Computational Linguistics; Artificial Intelligence (incl. Robotics); Electrical Engineering