Foundations of Statistical Natural Language Processing. It's used in psychotherapy, sports psychology and sales a lot]. According to research published on 11 July by three teams of scientists Universities of Hertfordshire, Edinburgh, and British Columbia NLP 'proponents' are being irresponsible in teaching people about eye movements.
Sentence boundary detection is a trivial issue in Natural Language Processing. Christopher D. Manning, Hinrich Schuetze Foundations. In part, this is because it could have been more selective in its choice of research paper summaries.
Of the many recent publications covered, some are surely, sadly, not destined to make a substantive impact on the field. The book also occasion- ally exhibits excessive reluctance to extract principles. One example of this reticence is its treatment of the work of Chelba and Jelinek ; although the text hails this paper as "the first clear demonstration of a probabilistic parser outperforming a tri- gram model" p.
Implicit in all of these comments is the belief that a mathematical foundation for statistical natural language processing can exist and will eventually develop. The authors, as cited above, maintain that this is not currently the case, and they might well be right. I cannot help but remember, in concluding, that I once read a review that said something like the following: "I k n o w y o u ' r e going to see this movie.
It doesn't matter what m y review says. Luckily, this big book takes its responsibilities seriously, and the authors are to be c o m m e n d e d for their efforts.
But it is worthwhile to r e m e m b e r that there are uses for both Big Books and Little Books. One of m y colleagues, a computational chemist with a b a c k g r o u n d in statistical physics, recently became interested in applying m e t h o d s from statistical NLP to protein modeling. Intrigued, he asked for a reference; he w a n t e d a source that w o u l d compactly introduce fundamental principles that he could adapt to his application.
I gave him Charniak References other myths of the statistical natural Allen, James. Natural Language language processing revolution. Invited Understanding. Second edition. Benjamin talk Fifteenth National Conference on Cummings. Charniak, Eugene. Statistical Language Jelinek, Frederick. Statistical Methods Learning.
The MIT Press. Chelba, Ciprian and Frederick Jelinek. Jurafsky, Daniel and James Martin. Exploiting syntactic structure for language Speech and Language Processing. This foundational text is the first comprehensive introduction to statistical natural language processing NLP to appear.
The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.
The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications. Statistical natural-language processing is, in my estimation, one of the most fast-moving and exciting areas of computer science these days.
0コメント