1 Introduction to NLP Tools 09/23/2003. 2 Motivation Machine Translation –From English to French What’s needed?

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Introduction to NLP Tools

09/23/2003

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Motivation

• Machine Translation– From English to French

• What’s needed?

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Motivation Cont’d (1)

• Syntactic parser• Part-Of-Speech Tagger

– Example: NP -> adj noun

• Morphological Analyzer– Example: “tools” -> “tool”

“Who is he?” -> “Who is he ?”

• Semantic Analyzer – Word sense disambiguate (“wash dishes”)– Choose the correct translation

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Motivation Cont’d (2)

• Lexicons– The information of the word

How many senses? What’s the possible translations

of the word?

• Corpus– Useful for learning a tool– Useful for evaluation

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Outline

• Lexicons

• Text corpora

• Morphological tools

• Part-Of-Speech(POS) taggers

• Syntactic parsers

• Semantic knowledge bases and semantic parser

• Speech tools

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Lexicons

• Definition– A repository for words

• Lexicons in LDC(Linguistic Data Consortium)– creating and sharing linguistic resources: data,

tools and standards.

• CELEX

• WordNet

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CELEX

• Dutch Center for Lexical Information• Lexical databases of English , Dutch and German• 21,000 nouns, 8,000 adjectives and 6,000 verbs• English:

– English Orthography, Lemmas– English Phonology, Lemmas– English Morphology, Lemmas– English Syntax, Lemmas– English Frequency, Lemmas– English Orthography, Wordforms– English Phonology, Wordforms– English Morphology, Wordforms– English Frequency, Wordforms– English Corpus Types– English Frequency, Syllables

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WordNet

• A database of lexical relations• Inspired by current psycholinguistic

theories of human lexical memory• Synset: a set of synonyms, representing one

underlying lexical concept– Example:

• fool {chump, fish, fool, gull, mark, patsy, fall guy, sucker, schlemiel, shlemiel, soft touch, mug}

• Relations link the synsets: hypernym, Has-Member, Member-Of, Antonym, etc.

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WordNet Cont’d

• Examplepu-erh.cs.utexas.edu$ wn bike -partn

Part Meronyms of noun bike

2 senses of bike

Sense 1

motorcycle, bike

HAS PART: mudguard, splashguard

Sense 2

bicycle, bike, wheel

HAS PART: bicycle seat, saddle

HAS PART: bicycle wheel

HAS PART: chain

HAS PART: coaster brake

HAS PART: handlebar

HAS PART: mudguard, splashguard

HAS PART: pedal, treadle, foot lever

HAS PART: sprocket, sprocket wheel

• ExamplePu-erh.cs.utexas.edu$wn bike

Information available for noun bike

-hypen Hypernyms

-hypon, -treen Hyponyms & Hyponym Tree

-synsn Synonyms (ordered by frequency)

-partn Has Part Meronyms

-meron All Meronyms

-famln Familiarity & Polysemy Count

-coorn Coordinate Sisters

-simsn Synonyms (grouped by similarity of meaning)

-hmern Hierarchical Meronyms

-grepn List of Compound Words

-over Overview of Senses

Information available for verb bike

-hypev Hypernyms

-hypov, -treev Hyponyms & Hyponym Tree

-synsv Synonyms (ordered by frequency)

-famlv Familiarity & Polysemy Count

-framv Verb Frames

-simsv Synonyms (grouped by similarity of meaning)

-grepv List of Compound Words

-over Overview of Senses

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Corpus

• Definition– Collections of text and speech

• LDC

• Penn Treebank

• DSO

• Hansard

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Some of the Top Corpus from LDC

• TIPSTER – Information Retrieval, Data Extrraction datasets– TIPSTER project, TREC project

• TIMIT Acoustic-Phonetic Continuous Speech Corpus– A corpus of read speech designed to – Provide speech data for the acquisition of acousticphonetic

knowledge – Useful for the development and evaluation of automatic speech

recognition systems• ECI(European Corpus Initiative Multilingual Corpus) multilingual

electronic text corpus• NTIMIT

– A phonetically– balanced, continuous speech, telephone bandwidth speech database

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Penn Treebank

• A collection of corpora• Tagged with POS, Syntactic roles,

predicate/argument structure, dysfluency annotation

• How are they made– Hand correction of the output of an errorful automatic

process

• 3 million words– 1 million words tagged with predicate/argument

structure for extraction semantic knowledge

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Penn Treebank Cont.’d

• Corpora– Wall Street Journal

– ATIS (Air Travel Information System)

– Brown Corpus

– IBM Manual Sentences

– Library of America Texts: Mark Twain, Henry Adams, Herman Melville ...

– MUC-3 Messages

• Example:( (S (NP-SBJ Rally 's)

(VP operates

and

franchises

(NP (NP (QP about 160)

fast-food restaurants)

(PP-LOC throughout

(NP the U.S))))

Seeking/VBG to/TO block/VB

[ the/DT investors/NNS ]

from/IN buying/VBG

[ more/JJR shares/NNS ]

./.

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DSO

• Word Sense Corpus– Contains sentences in which about 192,800

word occurrences have been tagged with WordNet senses

– Taken from the Brown corpus and the Wall Street Journal corpus

– 121 nouns and 70 verbs

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Hansard

• Official records (Hansards) of the 36th Canadian Parliament, both in English of French

• 1.3 million pairs of aligned sentences of English and French– Example

• Comme il est 14 h 30, la Chambre s'ajourne jusqu'\xe0 lundi prochain, \xe0 11 heures, conform\xe9ment au paragraphe 24(1) du R\xe8glement.

• It being 2.30 p.m., the House stands adjourned until Monday next at 11 a.m., pursuant to Standing Order 24(1).

• Useful for Machine Translation

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Morphological Tools

• PC-KIMMO– A two-level morphological parser

• Porter Stemmer

• Penn Treebank Tokenizer– Seperate document into words– “dog?” -> “dog ?”

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Porter Stemmer

• Simple algorithm, use a set of cascaded rewrite rules– Example

• Ational->ATE (relational->relate)

• Stem:– The main morpheme of the word, supplying the main

meaning

• Fast• Used very widely in Information Retrieval

– Run stemmer on keywords and the words in the documents

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Part-Of-Speech(POS) Taggers

• Part-Of-Speech: noun, verb, pronoun, etc.• Brill’s Tagger• HMM Tagger• MXPOST

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Brill’s Tagger

• Transformation-Based Learning(TBL) tagger• /projects/nlp/brill-pos-tagger• First labels every word with its most-likely tag• Then Use Learned TBL Rules to correct mistakes

– Example:• Change NN to VB when the previous tag is TO

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HMM Tagger

• Also called Maximum Likelihood Tagger• Xerox PARC's HMM tagger: ftp://parcftp.xerox

.com/pub/tagger/• Choose the tag sequence with the maximum

possibility given the words seen.

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MXPOST: Maximum Entropy POS Tagger

• Maximum Entropy Model is a framework integrating many information sources(called features) for classification

• Each candidate tag is a class• Given features of the word(the around words, the

morphological feature, and around tags, etc.), decide which class it belongs.

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Syntactic Parsers

• Collin’s Parser

• XTAG

• MXPOST: Maximum Entropy Parser

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Collin’s Parser

• Context-free Grammar

• Use frequencies to solve ambiguities

• Got some idea of this parser– Web-based Chart parser

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XTAG

• An on-going project to develop a wide-coverage grammar for English

• using a lexicalized Tree Adjoining Grammar (TAG) formalism– Context sensitive grammar

• consists of a parser, an X-windows grammar development interface and a morphological analyzer.

• /projects/nlp/xtag/

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XTAG Cont’d

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Semantic Knowledge Bases and Semantic Parser

• Analyze what does it say

• WordNet

• Penn Treebank

• Web-based Semantic Parser

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WordNet

• Respresents lexical relations

• Useful in word sense disambiguation

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Penn Treebank

Predicate: fool(Kris)

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Semantic Parser

• A web-based chart parser enriched with semantic constraints

• Example:– Input: My dog has fleas.– Output: has(my(dog),fleas)

•

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Speech Tools

• ISIP

• EPOS

• CSLU Toolkit

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ISIP

• ISIP(Institute for Signal and Information Processing) public domain speech recognition system

• Open research software

• Online courses, tutorials, dictionaries, databases

• Build your own speech recognition system

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EPOS

• a language independent rule-driven Text-to-Speech (TTS) system

• supports several main speech generation algorithms

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CSLU Toolkit

• Basic framework and tools for people to build, investigate and use interactive language systems

• speech recognition, natural language understanding, speech synthesis and facial animation technologies

• Easy to use , spread from higher education into homes

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Thanks!