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Natural Language Processing with Python Paperback – 10 Jul 2009
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Analyzing Text with the Natural Language Toolkit
About the Author
Steven Bird is Associate Professor in the Department of Computer Science and Software Engineering at the University of Melbourne, and Senior Research Associate in the Linguistic Data Consortium at the University of Pennsylvania. He completed a PhD on computational phonology at the University of Edinburgh in 1990, supervised by Ewan Klein. He later moved to Cameroon to conduct linguistic fieldwork on the Grassfields Bantu languages under the auspices of the Summer Institute of Linguistics. More recently, he spent several years as Associate Director of the Linguistic Data Consortium where he led an R&D team to create models and tools for large databases of annotated text. At Melbourne University, he established a language technology research group and has taught at all levels of the undergraduate computer science curriculum. In 2009, Steven is President of the Association for Computational Linguistics.
Ewan Klein is Professor of Language Technology in the School of Informatics at the University of Edinburgh. He completed a PhD on formal semantics at the University of Cambridge in 1978. After some years working at the Universities of Sussex and Newcastle upon Tyne, Ewan took up a teaching position at Edinburgh. He was involved in the establishment of Edinburgh's Language Technology Group in 1993, and has been closely associated with it ever since. From 2000-2002, he took leave from the University to act as Research Manager for the Edinburgh-based Natural Language Research Group of Edify Corporation, Santa Clara, and was responsible for spoken dialogue processing. Ewan is a past President of the European Chapter of the Association for Computational Linguistics and was a founding member and Coordinator of the European Network of Excellence in Human Language Technologies (ELSNET).
Edward Loper has recently completed a PhD on machine learning for natural language processing at the the University of Pennsylvania. Edward was a student in Steven's graduate course on computational linguistics in the fall of 2000, and went on to be a TA and share in the development of NLTK. In addition to NLTK, he has helped develop two packages for documenting and testing Python software, epydoc, and doctest.
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Top customer reviews
Having now worked my way through the book, lets take a look at how well it stands up to it's claims. Bad news first. The coverage of Python really didn't work for me, though I admit that this may be due to my background as a procedural rather than object oriented programmer. Without additional Python resources I was seriously struggling so if you are a complete Python and OO neophyte like me then I would strongly suggest working through either a good Python book or one of the many online tutorials before you attempt to tackle Natural Language Processing with Python. The other main problem was the amount of information that the book tries to cover. I found it helpful to scan read the book to get it into some sort of order in my mind before reading it any depth.
If I do have any other criticism of Natural Language Processing with Python, it's probably that it could probably be more accurately titled something like "Natural Language Toolkit: The Missing Manual".
Right that's the bad news out of the way, now for the good. The actual coverage of NLTK and, to a slightly lesser degree, natural language processing is excellent. The theme of the book is very pragmatic and task centred, so if you have a specific problem in mind which you feel needs a natural language approach then this book could well be the answer to your prayers. On the other hand if you are looking for a more theoretical overview of the subject you may be slightly disappointed. Natural Language Processing with Python certainly covers pretty much all the bases from comparatively simple statistical analysis, through context free grammar parsing and text classification all the way to discourse analysis. OK, some may complain that it's a bit code heavy and theory light but, when you consider that pretty much every chapter in the book has had several large tomes dedicated you can see what an achievement this book is.
In summary; if you have a particular problem that you want to use NLTK for but can't get your head round either the problem or the software buy Natural Language Processing with Python now - your frontal lobes will thank you forever. If you are interested in the field, and especially if you come from a pragmatic viewpoint or are already a Python hacker then you certainly won't be wasting your time or money. If you are terrified by the concept of programming and want an overview of the theory of linguistic analysis then there are probably better books for you out there.
The chapters on information extraction, parsing, semantics and managing linguistic data go beyond typical text mining books that only teach bag-of-word approaches and statistical sequence tagging in that logical/propositional semantics and discourse are covered from context-free grammars for parsing sentences to Discourse Representation Theory with lambda calculus for handling the composition of sentence semantics to discourse units, and dealing with the scope of quantifiers. The application of analyzing meaning is shown in a chapter on a toy database, which can be queried in natural language.
Two areas that would be nice to cover in future editions are statistical parsing and statistical machine translation.
For a future second edition, I'd also suggest the authors include an appendix "Hacking NLTK" about the internals of NLTK and how to extend it, to promote development of their tool further.
To sum, the book can be highly recommended to the student or teacher of natural language processing who would like to get practical experience rather than just study dry pseudocode.
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