《自然语言处理》神级教材：第二版精华解析

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16 Chapter 1. Introduction

gence (IJCAI) meetings. Artif cial intelligence journals that periodically feature work

on speech and language processing include Machine Learning, Journal of Machine

Learning Research, and the Journal of Artiﬁcial Intelligence Research.

There are a fair number of textbooks available covering various aspects of speech

and language processing. Manning and Sch¨utze (1999) (Foundations of Statistical Lan-

guage Processing) focuses on statistical models of tagging, parsing, disambiguation,

collocations, and other areas. Charniak (1993) (Statistical Language Learning) is an

accessible, though older and less-extensive, introduction to similar material. Manning

et al. (2008) focuses on information retrieval, text classif cation, and clustering. NLTK,

the Natural Language Toolkit (Bird and Loper, 2004), is a suite of Python modules

and data for natural language processing, together with a Natural Language Process-

ing book based on the NLTK suite. Allen (1995) (Natural Language Understanding)

provides extensive coverage of language processing from the AI perspective. Gazdar

and Mellish (1989) (Natural Language Processing in Lisp/Prolog) covers especially

automata, parsing, features, and unif cation and is available free online. Pereira and

Shieber (1987) gives a Prolog-based introduction to parsing and interpretation. Russell

and Norvig (2002) is an introduction to artif cial intelligence that includes chapters on

natural language processing. Partee et al. (1990) has a very broad coverage of mathe-

matical linguistics. A historically signif cant collection of foundational papers can be

found in Grosz et al. (1986) (Readings in Natural Language Processing).

Of course, a wide-variety of speech and language processing resources are now

available on the Web. Pointers to these resources are maintained on the home-page for

this book at:

http://www.cs.colorado.edu/˜martin/slp.html.

Section 1.7. Summary 17

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ing in LISP. Addison Wesley.

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(1986).

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mann, Los Altos, Calif.

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18 Chapter 1. Introduction

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Speech and Language Processing: An introduction to natural language processing,

computational linguistics, and speech recognition. Daniel Jurafsky & James H. Martin.

without permission.

REGULAR EXPRESSIONS

AND AUTOMATA

User

: Men are all alike.

ELIZA

: IN WHAT WAY

User

: They’re always bugging us about something or other.

ELIZA

: CAN YOU THINK OF A SPECIFIC EXAMPLE

User

: Well, my boyfriend made me come here.

ELIZA

: YOUR BOYFRIEND MADE YOU COME HERE

User

: He says I’m depressed much of the time.

ELIZA

: I AM SORRY TO HEAR YOU ARE DEPRESSED.

Weizenbaum (1966)

Imagine that you have become a passionate fan of woodchucks. Desiring more in-

formation on this celebrated woodland creature, you turn to your favorite Web browser

and type in woodchuck. Your browser returns a few sites. You have a f ash of inspira-

tion and type in woodchucks. This time you discover “interesting links to woodchucks

and lemurs” and “all about Vermont’s unique, endangered species”. Instead of having

to do this search twice, you would have rather typed one search command specify-

ing something like woodchuck with an optional ﬁnal s. Or perhaps you might want

to search for all the prices in some document; you might want to see all strings that

look like

199 or

25 or

24.99. In this chapter we introduce the regular expression,

the standard notation for characterizing text sequences. The regular expression is used

for specifying text strings in situations like this Web-search example, and in other in-

formation retrieval applications, but also plays an important role in word-processing,

computation of frequencies from corpora, and other such tasks.

After we have def ned regular expressions, we show how they can be implemented

via the ﬁnite-state automaton. The f nite-state automaton is not only the mathemati-

cal device used to implement regular expressions, but also one of the most signif cant

tools of computational linguistics. Variations of automata such as f nite-state trans-

ducers, Hidden Markov Models, and N-gram grammars are important components of

applications that we will introduce in later chapters, including speech recognition and

synthesis, machine translation, spell-checking, and information-extraction.

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《自然语言处理》神级教材：第二版精华解析

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