Natural Language Processing Using Very Large Corpora

Natural Language Processing Using Very Large Corpora

5 (1 rating by Goodreads)
Edited by  , Edited by  , Edited by  , Edited by  , Edited by  , Edited by 

Free delivery worldwide

Available. Dispatched from the UK in 3 business days
When will my order arrive?

Description

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.
show more

Product details

  • Hardback | 305 pages
  • 166.1 x 246.4 x 23.9mm | 657.72g
  • Dordrecht, Netherlands
  • English
  • 1999 ed.
  • XV, 305 p.
  • 0792360559
  • 9780792360551

Table of contents

Introduction. Implementation and Evaluation of a German HMM for POS Disambiguation; H. Feldweg. Improvements in Part-of-Speech Tagging with an Application To German; H. Schmid. Unsupervised Learning of Disambiguation Rules for Part-of-Speech Tagging; E. Brill, M. Pop. Tagging French without Lexical Probabilities - Combining Linguistic Knowledge and Statistical Learning; E. Tzoukermann, et al. Example-Based Sense Tagging of Running Chinese Text; X. Tong, et al. Disambiguating Noun Groupings with Respect to WordNet Senses; P. Resnik. A Comparison of Corpus-based Techniques for Restoring Accents in Spanish and French Text; D. Yarowsky. Beyond Word N-Grams; F. Pereira, et al. Statistical Augmentation of a Chinese Machine-Readable Dictionary; P. Fung, D. Wu. Text Chunking Using Transformation-based Learning; L. Ramshaw, M.P. Marcus. Prepositional Phrase Attachment through a Backed-off Model; M. Collins, J. Brooks. On the Unsupervised Induction of Phrase-Structure Grammars; C. de Marcken. Robust Bilingual Word Alignment for Machine Aided Translation; I. Dagan, et al. Iterative Alignment of Syntactic Structures for a Bilingual Corpus; R. Grishman. Trainable Coarse Bilingual Grammars for Parallel Text Bracketing; D. Wu. Comparative Discourse Analysis of Parallel Texts; P. van der Eijk. Comparing the Retrieval Performance of English and Japanese Text Databases; H. Fujii, W.B. Croft. Inverse Document Frequency (IDF): A Measure of Deviations from Poisson; K. Church, W. Gale. List of Authors. Subject Index.
show more

Rating details

1 ratings
5 out of 5 stars
5 100% (1)
4 0% (0)
3 0% (0)
2 0% (0)
1 0% (0)
Book ratings by Goodreads
Goodreads is the world's largest site for readers with over 50 million reviews. We're featuring millions of their reader ratings on our book pages to help you find your new favourite book. Close X