Row Row Row Your Boat….

==Foundations of Statistical Natural Language Processing==

Chapters

Foundations of Statistical Natural Language Processing
Christopher D. Manning and Hinrich Schütze

	List of Tables	xv
	List of Figures	xxi
	Table of Notations	xxv
	Preface Sample Chapter - Download PDF (85 KB)	xxix
	Road Map Sample Chapter - Download PDF (94 KB)	xxxv
I	Preliminaries	1
1	Introduction Sample Chapter - Download PDF (263 KB)	3
2	Mathematical Foundations	39
3	Linguistic Essentials	81
4	Corpus-Based Work	117
II	Words	149
5	Collocations	151
6	Statistical Inference: n-gram Models over Sparse Data	191
7	Word Sense Disambiguation	229
8	Lexical Acquisition	265
III	Grammar	315
9	Markov Models	317
10	Part-of-Speech Tagging	341
11	Probabilistic Context Free Grammars	381
12	Probabilistic Parsing	407
IV	Applications and Techniques	461
13	Statistical Alignment and Machine Translation	463
14	Clustering	495
15	Topics in Information Retrieval	529
16	Text Categorization	575
	Tiny Statistical Tables	609
	Bibliography Sample Chapter - Download PDF (263 KB)	611
	Index Sample Chapter - Download PDF (130 KB)	657

(From introduction) The aim of a linguistic science is to be able to characterize and explain the multitude of linguistic observations circling around us, in conversations, writing, and other media. Part of that has to do with the cognitive side of how humans acquire, produce, and understand language, part of it has to do with understanding the relationship between linguistic utterances and the world, and part of it has to do with understanding the linguistic structures by which language communicates. In order to rules approach the last problem, people have proposed that there are rules which are used to structure linguistic expressions. This basic approach has a long history that extends back at least 2000 years, but in this century the approach became increasingly formal and rigorous as linguists explored detailed grammars that attempted to describe what were wellformed versus ill-formed utterances of a language.(From introduction)

=Common American Phrases in Everyday Context=



=Garfield=