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Natural Language Processing

Applications

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aims at :

making computers talk

ability of humans

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Dialog system

Fiction

C3PO -- Star Wars-- space o yssey

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Dialog system

reality

E-commerce: AINI

a c a er o n egra e

with 3D animated agent

Improve customer

Reduce customer reliance

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Dialog system

reality

-

(http://www.autotutor.org/what/what.htm )

learn by holding a conversationnal in natural

lan ua e

Animated agent : synthesis speech,

intonation, facial ex ressions, and estures

demo (from 2002)

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-

Ask complete questions

a oes vergen mean

How many states were in Europe in 2007? What is the occupation of Bill Clintons wife ?

What do scientist think about global warming?

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What would HAL need to engage in this dialog?

Dave Bowman: Hello, HAL do you read me, HAL? HAL: Affirmative, Dave, I read you.

Dave Bowman: O en the od ba doors, HAL.

HAL: I'm sorry Dave, I'm afraid I can't do that.

Dave Bowman: What's the problem?

HAL: I think you know what the problem is just as well as I do. Dave Bowman: What are you talking about, HAL?

HAL: This mission is too important for me to allow you tojeopardize it.

' ' ,

HAL: I know you and Frank were planning to disconnect me, andI'm afraid that's something I cannot allow to happen.

Dave Bowman: Where the hell'd you get that idea, HAL?

HAL: Dave, although you took thorough precautions in the pod

against my hearing you, I could see your lips move.

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Speech recognition / speech

synthesis

how words are pronounced in terms ofsequences of sounds

How each of these sounds is realized

acoustically Morphology : cant, Im, were, lips...

Producing and recognizing variations of

n v ua wor s The way words break down into component

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Phonetics

speech

/i:/, /:/, /:/, /:/ and /u:/

'there' => /e/

'there on the table' => /ern tebl /

Exercices

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Articulory phonetics : production

Acoustics phonetics: properties of soundwaves (frequency and harmonics)

McGurk effect

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Describe the way sounds function to encode meaning

Phoneme : speech sound that helps us constructing

meaning

, , , .

/u/ : rubble rabble, rebel, Ribble, robble...

Phoneme can be realized in different forms depending on

context (allophones)

Speech synthesis uses allophones

Speackjet

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Study the structure of words

walks, walking, walked walk

Lemma + part of speech = lexeme Walk, walking, walked walk

,

Flectional morphology : decomposes a word into a lemma and one

or more affixes giving informations abouts tense, gender, number

Derivational morphology: decomposes a word into a lemma and oneor more affixes giving informations about meaning and category

,

Exceptions and irregularities ? Women woman, pl

ArentAre not

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Morphology

Methods Lemmatisation : process of grouping together the different

inflected forms of a word so they can be analysed as asingle item Need to determine the art of s eech of a word in a sentence

(requiring grammar knowledge)

Stemming: operates on a single word without knowledge of

cannot discriminate between words which have different meaningsdepending on part of speech

,

for some applications

Examples ,

walkinglemma: walk, matched in both stemming andlemmatization.

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Morphology

Method and applications

Finite state transducer

to resolve anaphora:

Sarah met the women in the street..

women (pl) ]

for spell checking and for generation

* The women (pl) is (sg)

For information retrieval

...

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Im sorry Dave, I cant do that

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Syntax

structure of language

,

anguages ave structure:

not all sequences of words over the givena p a et are va

when a sequence of words is valid

gramma ca , a na ura s ruc ure can einduced on it.

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expressions I am sorr Dave I cant do that

Grammars are used to describe the syntax

of a lan ua e Syntactic analysers and surface realisers

assi n a s ntactic structure to a

string/semantic representation on thebasis of a grammar

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tree:

according to some formal grammar.

-

terminals of the grammar, while the leaf

nodes are labeled b terminals of the

grammar.

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Syntax

tree example

S

NP VP

V NP PPJohn

Adv NPV Det Prepn

often gives a book to Mary

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Words syntactic tree

Algorithm: parser A parser checks for correct syntax and builds a data.

Resources used: Lexicon + Grammar

-

Statistical : grammar acquired from treebank Treebank : text corpus in which each sentence has

been annotated with syntactic structure. Syntactic structure is commonly represented as a tree

Difficulty: coverage and ambiguity

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Syntax

applications

For spell checking*

Its a fair exchange ok syntactic tree

To construct the meaning of a sentence

To generate a grammatical sentence

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John loves Mary love(j,m)gen = u ec

Mary loves John love(m,l)gent = u ect

=Mary is loved by John love(j,m)

Agent = By-Object

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Where the hell d you get that idea HAL

Dave, although you took thorough precautions

n e po aga ns my ear ng you, cou seeyour lips move

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Lexical semantics

Meaning of wordsAn idea

1. come to have or hold;receive.

2. succeed in attaining,

. a oug or sugges on a ou a

possible course of action.2. a mental impression.

ac ev ng, or exper enc ng;obtain.

3. experience, suffer, or be

afflicted with.

. .

4. (the idea) the aim or purpose.

. move n or er o p c up,deal with, or bring.

5. bring or come into aspecified state or condition.

1. a place regarded in various religions

as a spiritual realm of evil and

suffering, often depicted as a place. ca c , appre en , or war .

7. come or go eventually orwith some difficulty.

8. move or come into a

of perpetual fire beneath the earth towhich the wicked are sent after

death.

spec e pos on or s a e

...

. .

3. a swear word that some people use

when they are annoyed or surprised

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Who is the master?

Context?

Semantic relations?

Lewis Carroll, Through the looking glass

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Where the hell did you get that idea?

a swear word that some people

use when they are annoyed or

surprised or to emphasize sth Have this belief

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Definition and representation of meaning

Semantic relations Interaction between semantic and syntax

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Semantic relations

Paradigmatic relation (substitution) Blablabla word1 bla bla bla

word2

How are youdoing? I would ask.Ask me how I amfeeling? he answered. , . . .I am veryhappyand verysad.How can you be both at the same time? I asked in all seriousness, a girl of

nine or ten.Because both require each others company. They live in the same house.Didnt you know?

Terry Tempest Williams, The village watchman (1994)

synonymy: sofa=couch=divan=davenport

antonymy: good/bad, life/death, come/go

contrast: sweet/sour/bitter/salt , solid/li uid/ as

hyponymy, or class inclusion: cat

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Syntagmatic relations: relations between words that go

together in a syntactic structure.

Collocation : heavy rain, to have breakfast, to deeply regret...

Argumental structure

Someone breaks something with something

3 arguments

Difficulty: number of arguments ? Can an argument be optional ?John brokes the window

John brokes the window with a hammer

The window brokessemantic argument syntactic argument

Thematic roles : agent, patient, goal, experiencer, theme...

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semantic / syntax

lexicon

Sub categorisation frames o run:

to eat : SN1, SN2

To give : SN1, SN2, SP3 to

envious : SN1, SP2 (of)

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Semantic / syntax

lexicon

Logic representation: eat (x, y), give (x,y,z) Thematic roles : to give [agent, theme, go k], to buy [agent,theme, source , to love ex eriencer, atient

Link with syntax: break (Agent:, Instrument, Patient:) Agent subj

Instrument subj, with-pp Patient obj, subj

Selectional restrictions: semantics features on arguments

John eats bread lthme [+solide] [+comestible] *The banana eats filtering * John eats wine

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Le robinetfuit/ Le voleurfuit-> leak/run away

For information retrieval (and cross Language

Information Retrieval) Search on word meaning rather than word form

Keywords disambiguation

more relevance

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QA: Who assassinated President McKinley?

Person / Answer thematic role : Agent of target synonymous with

\assassinated

False positive (1): In [ne=date 1904], [ne=person descriptionPresident] [ne=person Theodore Roosevelt], who had succeeded the[target assassinated] [role=patient [ne=person William McKinley]], waselected to a term in his own right as he defeated [ne=person description

Democrat] [ne=person Alton B. Parker]? Correct Answer (8): [role=temporal In [ne=date 1901]], [role=patient

[ne=person description President] [ne=person William McKinley]] was[target shot] [role=agent by [ne=person description anarchist][ne=person Leon Czolgosz]] [role=location at the [ne=event Pan-

mer can xpos on n ne=us c y u_a o , ne=us s a e . .

Using Semantic representation in question answering, Sameer S and al, 2003

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Dave Bowman: Open the pod bay doors, HAL.

HAL: I'm sorry Dave, I'm afraid I can't do that.

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speakers intend by their use of sentences , .

STATEMENT: HAL, the pod bay door is open.

,

bay door open?

, ,greeting, apologizing...)

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Where the hell'd ou et that idea HAL?

Dave and Frank were planning to disconnect me

Much of language interpretation is dependenton the preceding discourse/dialogue

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Linguistics knowledge in NLP

summary

Phonetics and Phonology knowledge about linguisticsounds

orp o ogy now e ge o e mean ng u componen sof word

Syntax knowledge of the structural relationshipsbetween word

Semantics knowledge of meaning

to the goals and intentions of the speaker Discourse knowledge about linguistic units larger than

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processing can be viewed as resolving

Ambiguous item multiple, alternative

linguistic structures can be built for it.

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coo e water ow or er.

I cooked waterfowl belonging to her. I created the (plaster?) duck she owns.

body.

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duck : verb / noun

Part of speech

tagging

Semantical ambiguity

Make: create / cookWord sense

disambiguation

Syntatic ambiguity:

Make: transitive/ ditransitive Syntactic

[her duck ] / [her][duck]disambiguation /

parsing

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- -

Recognise speech. .

Speech act interpretation

Can you switch on the computer?

Question or request?

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Ambi uit : the same sentence can mean

different things

Paraphrase: There are many ways of saying the.

Beer, please.

Can I have a beer? ve me a eer, p ease. I would like beer. Id like a beer, lease.

In generation (MeaningText), this implies makingchoices

Combinatorial problem

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captured through the use of a small

o e s an eor es are a rawn or e

standard toolkit of computer science,

ma ema cs an ngu s cs

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State machines Rule systems

Dynamic programming Machine learning

Logic

Probalistic models

Classifiers / sequence

models

- Vector-space models maximization (EM)

Learning algorithms

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State, transition among state, input

Finite-state automata

Non deterministic

Finite-state transducers

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egu ar grammars

Context-free grammars

Feature augmented grammars

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the main tools used when dealing with, ,

syntax.

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First Order Logic / predicate calculus Lamda-calculus, feature structures, semantic

primitives

These logical representations have traditionallybeen used for modeling semantics and

pragmatics, although more recent work has

techniques drawn from non-logical lexical

semantics.

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Probabilistic models crucial for capturing every kind of linguistic knowledge.

Each of the other models can be augmented with probabilities. Exam le the state machine au mented with robabilities can

become

weighted automaton, orMarkov model.

hidden Markov models (HMMs) : part-of-speech tagging, speechrecogn on, a ogue un ers an ng, ex - o-speec , mac netranslation....

Key advantage of probabilistic models : ability to solve

almost any speech and language processing problem can berecast as given Nchoices for some ambiguous input, choose

.

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based on linear algebra -

Word meanings

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states representing hypotheses about an input Speech recognition : search through a space of

phone sequences for the correct word.

Parsing : search through a space of trees for the

.

Machine translation : search through a space of

translation hypotheses for the correct translation of a

sentence into another language.

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Machine learning models: classifiers, sequence models Based on attributes describing each object

Classifier : attempts to assign a single object to a single class Se uence model: attem ts to ointl classif a se uence of

objects into a sequence of classes.

Example, deciding whether a word is spelled correctly : classifiers : decision trees, support vector machines, Gaussian

mixture models + logistic regression make a binary decision(correct or incorrect) for one word at a time.

,

Markov models + conditional random fields assigncorrect/incorrect labels to all the words in a sentence at once.

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-

1950- 1970 : symbolic / statistical : our para gms

1983 1993 : empiricism and finite statemodels

1994 1999: field unification

2000 -2008 : empiricist trends

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Probabilistic / information theoretic

1940s 1950s

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1940 s 1950 s

Automaton Turings (1936) : model of algorithmic computation

McCulloch-Pitts neuron (McCulloch and Pitts, 1943) : a simplified

model of the neuron as a kind of computing element (propositionallogic)

Kleene (1951) and (1956) : finite automata and regular expressions.

Shannon (1948) : probabilistic models of discrete Markov processes.

Chomsky (1956) : finite state machines as a way to characterize agrammar

Context-free grammar for natural languages Chomsky (1956)

Backus (1959) and Naur et al. (1960) : ALGOL programming language.

1940s 1950s

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1940 s 1950 s

Probalistic algorithms,

Shannon metaphor of the noisy channel

en ropy as a way o measur ng e n orma on capac y o achannel, or the information content of a language,

first measure of the entropy of English by using probabilistic

.

Sound spectrograph (Koenig et al., 1946),

Foundational research in instrumental phonetics

First machine speech recognizers (early 1950s). 1952, Bell Lab, statistical system that could recognize any of the10 digits from a single speaker (Davis et al., 1952).

1940s 1950s

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1940 s 1950 s

Machine translation

Major attempts in US and USSR

George Town University, Washington system:

The ALPAC report (1964) u u w

Concluded: MT not possible in near future.

Funding should cease for MT !

Basic research should be supported.

Word to word translation does not work

Linguistic Knowledge is needed

1950s 1970s

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Two camps

Symbolic paradigm

1950s 1970s

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Symbolic paradigm 1Formal lan ua e theor and enerative s ntax

1957 Noam Chomsky's Syntactic Structures A formal definition of grammars and languages Provides the basis for an automatic syntactic

processing of NL expressions

' Formal semantics for NL.

Basis for logical treatment of NL meaning

1967 : Woods procedural semantics A procedural approach to the meaning of a sentence

Provides the basis for a automatic semanticprocessing of NL expressions

1950s 1970s

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Symbolic paradigm 2

Parsing algorithms - -

dynamic programming.

Project (TDAP)

Harris, 1962

Joshi and Hopely (1999) and Karttunen (1999), cascade of finite-state transducers.

1950s 1970s

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Symbolic paradigm 3

AI

Summer of 1956 :John McCarthy, Marvin Minsky,Claude Shannon and Nathaniel Rochester work on reasoning and logic

Newell and Simon the Logic Theorist and the General

systems

Domains

om na on o pa ern ma c ng an eywor searc

Simple heuristics for reasoning and question-answering

1950s 1970s

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Statistical paradigm 1 Ba esian method to the roblem of o tical character reco nition.

Bledsoe and Browning (1959) : Bayesian text-recognition

a large dictionary compute the likelihood of each observed letter sequence given each word inthe dictionary

Joshi and Hopely (1999) and Karttunen (1999) cascade of finite-state transducers likelihoods for each letter.

Bayesian methods to the problem of authorship attribution on The

Mosteller and Wallace (1964)

Testable psychological models of human language processing

Ressources First online corpora: the Brown corpus of American Englis

an on-line Chinese dialect dictionary.

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Symbolic

Based on hand written rules

Requires linguistic expertise

No frequencey information

Often more precise than statistical approaches

Error analysis is usually easier than for statistical approaches

Statistical

Supervised or non-supervised

Not much linguistic expertise required Robust and quick

Error analysis is often difficult

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1970-1983

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Statistical paradigmSpeech recognition algorithms

Hidden Markov model (HMM) and the metaphors of thenoisy channel and decoding

Jelinek, Bahl, Mercer, and colleagues at IBM s Thomas J.Watson Research Center,

Baker at Carnegie Mellon University

aum an co eagues at t e nst tute or e enseAnalyses in Princeton

AT&Ts Bell

Rabiner and Juang (1993) descriptions of the widerange of this work.

1970-1983

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Logic-based paradigm -

(Colmerauer, 1970, 1975)

Warren, 1980)

Functional grammar (Kay,1979)

Lexical Functional Grammar LFG

(Bresnan and Kaplans,1982)im ortance of feature structure unification

1970-1983

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Natural language understanding1

SHRDLU system : simulated a robot embedded

in a world of toy blocks (Winograd, 1972a).

natural-language text commands

Move the red block on top of the smaller green one

first to attempt to build an extensive (for the time)

grammar of English (based on Hallidays systemic

grammar)

Ok for parsing

eman c an scourse

1970-1983

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Natural language understanding2

Yale School : series of language

understanding programs

conceptual knowledge (scripts, plans, goals..)

human memor or anization network-based semantics (Quillian, 1968)

case roles Fillmore 1968

representations of case roles (Simmons,1973 .

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- - -

language-understanding paradigms in-

answering system (Woods, 1967, 1973)

uses predicate logic as a semantic

represen a on

1970-1983

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Discourse Modelling

Four key areas in discourse:

Substructure in discourse Grosz, 1977a,

A discourse focus

Automatic reference resolution Hobbs

Sidner, 1983

1978)

BDI Belief-Desire-Intention

framework for logic-based work on speechacts (Perrault and Allen,1980; Cohen andPerrault, 1979).

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- Return of state models

Finite-state phonology and morphology (Kaplan and Kay, 1981)

Finite-state models of syntax by Church (1980). Return of empiricism ro a s c mo e s roug ou speec an anguage process ng,

IBM Thomas J. Watson Research Center: probabilistic models of speechrecognition.

Data-driven approaches

- - , , ,semantics.

New focus on model evaluation, Held-out data

Quantitative metrics for evaluation,

Comparison of performance on these metrics with previous publishedresearch.

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-Major changes.

Probabilistic and data-driven models had become quite standard

Parsing, part-of-speech tagging, reference resolution, and discourseprocessing gor ms ncorpora e pro a es

Evaluation methodologies from speech recognition and informationretrieval.

commercial exploitation (speech recognition, spelling and grammar

correction)

Rise of the Web need for language-based information retrieval and information

extraction.

1994-1999

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Ressources and corpora

s space ecomes c eap

Machine readable text becomes uniquitous

on real data

1994 : The British National Corpus is madeava a e A balanced corpus of British English

Mid 199 s : WordNet Fellbaum & Miller

A computational thesaurus developed bypsycholinguists

2000-2008

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Empiricist trends 1

Linguistic Data Consortium (LDC) ...

Annotated collections (standard text sources with various forms ofsyntactic, semantic, and pragmatic annotations) enn ree an arcus e a ., ,

PropBank (Palmer et al., 2005),

TimeBank (Pustejovsky et al., 2003b)

....

ore comp ex ra ona pro ems cas a e n superv se mac nelearning

Parsing and semantic analysis

Competitive evaluations ars ng e ean an ong m ang, ,

Information extraction (NIST, 2007a; Tjong Kim Sang, 2002; Tjong Kim Sangand De Meulder,

2003)

., ,

Question answering (Voorhees and Tice, 1999), and summarization (Dang,2006).

2000-2008

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Empiricist trends 2

More serious interplay with the statistical

machine learning community Support vector machines (Boser et al., 1992;

Vapnik, 1995)

Maximum entropy techniques (multinomial

logistic regression) (Berger et al., 1996)

Graphical Bayesian models (Pearl, 1988)

2000-2008

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Empiricist trends 2

Largely unsupervised statistical approaches

(Brown et al., 1990; Och and Ney, 2003) t

To ic modelin Blei et al., 2003 Effective applications could be constructed

alone.

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Is a talking machine a thinking machine?

cognitive abilities.

" ,Intelligence

Imprecion in the terms machine and think

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The com uters use of lan ua e forms the basisfor determining if the machine could think.

If the machine could win the game, it would be.

Three participants two people and a computer.

a human judge engages in a natural languageconversation with one human and one machine

each of which try to appear human

If the judge cannot reliably tell which is which,then the machine is said to pass the test.

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program that mimics human conversation.

Jose h Weizenbaum Massachusetts Instituteof Technology

User types in natural language

ELIZA then analyzes the user's statementand generates some response which it

types out. http://www-ai.ijs.si/eliza/eliza.html

ELIZA

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Men are all alike.Sample conversation

They're always bugging us about something or other.

CAN YOU THINK OF A SPECIFIC EXAMPLE? Well, m bo friend made me come here.

YOUR BOYFRIEND MADE YOU COME HERE

He says I'm depressed much of the time.

I AM SORRY TO HEAR YOU ARE DEPRESSED

' . DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE

UNHAPPY

I need some help, that much seems certain.

WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP

Perhaps I could learn to get along with my mother. TELL ME MORE ABOUT YOUR FAMILY

Eliza's strategy

K d

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Keywords

keyword

When ke word is found the sentence is

transformed according to a rule associated with

the keyword

Else a content-free remark or, under certain

conditions, an earlier transformation is retrieved.

The text so computed or retrieved is then printedout

ELIZA

E l t f ti

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Example transformations

"I am X" "How long have you been X?I am very unhappy.

HOW LONG HAVE YOU BEEN VERY UNHAPPY?

"X YOU Y ME" "What makes you think I Y

you?

It seems that ou hate me.

WHAT MAKES YOU THINK THAT I HATE YOU?

ELIZA

Wh d it k?

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Why does it work?

Rogerian psychotherapist Can pretend toknow almost nothing of the real world

"I went for a long boat ride"

"Tell me about boats"

Not a real dialog (no real communication) ELiza generates a reaction (often a rather non

'

The user cannot ask for information. The user cannot issue an order

ELIZA

D it ll k?

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Does it really work? Eliza: Hello. I am ELIZA. How can I help you?

You: I went to Paris yesterday

Eliza: Can you elaborate on that? ou: oo e ra n

Eliza: Can you elaborate on that?

You: The train was late Eliza: Please go on.

You: All the seats were full

Eliza: Please go on.

You: A little boy was having a tantrum

.

ELIZA

D it ll k?

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Does it really work?, , ,

User: I am having fun right now.

Eliza: Does it lease ou to believe that I am havin

fun right now.

World KnowledgeUser: I am a human.

Eliza: How long have you been a human?

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Yes/No QuestionsUser: Are you a good therapist?

Eliza: Would you prefer it if I were a good therapist?

User: I'm wondering about your skills as a therapist;

therapist?

Wh-Questions

User: Who are ou?Eliza: Would you prefer it if I were not?

User: What do you want me to tell?

Eliza: Does that question interest you?

Complex Assertions

User: I don't have a problem. I'm quite happy, I have a girlfriend, Istudy. Everything is OK!

', ,girlfriend, you study. Everything is OK!

...

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computers, they talk about them and interact with them as social entities.

Weizenbaum (1976) notes that many people continued to believe inELIZAs abilities even after the ro rams o eration was ex lained tothem.

People act toward computers as if they were people and expect thatcomputers should be able to understand their needs and be capable of

n erac ng w em na ura y.

Given these predispositions, speech- and language-based system arenot supposed to be intelligent

But they may provide users with the most naturalinterface for many applications

So what about turin test?

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Three main types of applications: Language input technologies

Language output technologies

Language input technologies

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Speech recognition

Handwriting recognition

e roconvers on

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Two main types of Applications Deskto control: dictation, voice control, navi ation

Telephony-based transaction: travel reservation,remote banking, pizza ordering, voice control

- . Speech recognition is not understanding!

corporaCf. the Parole team Yves La rie

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Desktop control . . .

IBM ViaVoice (www.software.ibm.com/speech)

Scansoft's Dra onNaturall S eakin

(www.lhsl.com/naturallyspeaking)

demo

ee a so goog e ca egory:

http://directory.google.com/Top/Computers/Sp

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Dictation

Dictation systems can do more than just transcribe whatwas said: leave out the 'ums' and 'eh

implement corrections that are dictated

fill the information into forms

rephrase sentences (add missing articles, verbs and

corrections)

Communicate what is meant, not what is said

commands to the word processing applications (speechmacros eg to insert frequently used blocks of text or tonavi ate throu h form

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Telephony-based elded products . . ScanSoft (www.scansoft.com)

. . . Telstra directory enquiry (tel. 12455)

http://directory.google.com/Top/Computers/SpeechTechnology/Telephony/


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Optical character recognition

Key focus Printed material computer readable representation

A lications Scanning (text ) digitized format)

Business card readers (to scan the printed information frombusiness cards into the correct fields of an electronic address

. . Website construction from printed documents

Fielded products aere s mn agewww.scanso .com

Xerox' TextBridge (www.scansoft.com) ExperVision's TypeReader (www.expervision.com)



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Human handwriting computer readable

Applications

Forms processing

Mail routing

Personal digital agenda (PDA)

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Isolated letters

'

Computer Intelligence Corporation's Jot

(www.cic.com)

Cursive scripts

Motorola's Lexicaus

ParaGraph's Calligraphper (www.paragraph.com)

cf. the READ team (Abdel Belaid)

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Key focus: identify the logical and physical

Applications

Recognising bibliographical references

formulae Document classication

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Spoken Language Dialog System ac ne rans at on

Text Summarisation

Search and Information Retrieval

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Goal a system that you can talk to in order to carry out some task.

Key focus

Speech synthesis

Dialogue Management

Information provision systems: provides information in responseto query (request for timetable information, weather information)

-

buying/selling stocs or reserving a seat on a plane.

-

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-

based systems

User initiative remains limited (or likely to

resu n errors

SLDS

state of the art

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state of the art

limited transaction and information

Stock broking system

American Airlines information system

m e n e-s a e a ogue managemen

NL Understanding is poor

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. .

SpeechWorks (www.scansoft.com) ps www.speec .p ps.com

See also google category :

http://directory.google.com/Top/Computers/Sp

eechTechnology/

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Translating a text written/spoken in one

Applications

Spoken language translation services

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' . .

Taum-Meteo (1979): (English/French)

Highly successful

S stran: amon several Euro ean lan ua es

Human assisted translation

Rough translation

Used over the internet through AltaVista

http://babelsh.altavista.com

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on the web (Systran)

(TAUM Meteo) or controlled languages

ac ne a e rans a on s mos y use

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Text Shorter version of text

pp ca ons

To decide whether it's worth reading the

To read summary instead of full text

o au oma ca y pro uce a s rac

Three main ste s

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Three main ste s

1. Extract \important sentences" (compute

document keywords and score document

2. Cohesion check: Spot anaphoric referencesand modify text accordingly (eg add sentence

con a n ng pronoun an ece en ; remove cusentences; remove pronoun)

3. Balance and covera e: modif summar tohave an appropriate text structure (deleteredundant sentences; harmonize tense of

St t f th A t

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State of the Art Sentences extracted on the basis of: location,

linguistic cues, statistical information ow scourse co erence

Commercial systems

' . . . Copernic (www.copernic.com)

MS Word's Summarisation tool

See also

http://www.ics.mq.edu.au/~swan/summarization/projects.htm

Information Extraction / Retrieval

and AGiven a NL uer and a document e web

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Given a NL uer and a document e. . webpages),

Retrieve document containing answer (retrieval) n temp ate w t re evant n ormat on extract on

Produce answer to query (Q/A)

Excludes: how-to questions, yes-no questions,

uestions that re uire com lex reasonin

Highest possible accuracy estimated at around70%

Information Extraction / Retrieval

and A

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, , .

QA systems s eeves www.as eeves.com

Articial life's Alife Sales Rep (www.articial-

. Native Minds'vReps (www.nativeminds.com)

o oquy www.so oquy.com

Language output technologies

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Text-to-Speech

Tailored document generation

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Key focus

Applications

telephone

Document roofreadin

Voice portals Computer assisted language learning

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Requires appropriate use of intonation and

Existing systems

'(www.lhsl.com/realspeak)

British Telecom's Laureate

AT&T Natural Voices(http://www.naturalvoices.att.com)

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Key focus ocumen s ruc ure + parame ers

Individually tailored documents

pp ca ons Personalised advice giving

Customised policy manuals

Web delivered dynamic documents

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. .

Tailored job descriptions

o en ex www.cogen ex.com

Project status reports

Weather reports

NLP application

summary

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All levels of linguistic knowledge are relevant

Two main roblems: ambi uit and ara hrase

NLP applications use a mix of symbolic and statistical methods

Current applications are not perfect as

Symbolic processing is not robust/portable enough Statistical processing is not accurate enough

Applications should be classied into two main types: aids to humanusers e.g., spe c ec ers, mac ne a e rans a ons an agen sin their own right (e.g., NL interfaces to DB, dialogue systems)

Useful applications have been built since the late 70s

Commercial success is harder to achieve

htt ://cslu.cse.o i.edu/HLTsurve /HLTsurve .html

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htt ://cslu.cse.o i.edu/HLTsurve /HLTsurve .html

Speech and Language ProcessingAn introduction to Natural Language Processing,

Comptutational Linguistics, and Speech Recognition, by

Daniel Jurafsky and James H. Martin

dr. gv. uma 3

Documents