introduction to language technology potsdam, 12 april 2012 · introduction to language technology...

Natural Language ProcessingIntroduction to Language Technology

Potsdam, 12 April 2012

Saeedeh MomtaziInformation Systems Group

Outline

1 Administrative Information

2 Introduction

3 NLP Applications

4 NLP Techniques

5 Linguistic Knowledge

6 Challenges

7 Course Materials

Saeedeh Momtazi | NLP | 12.04.2012

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Outline


2 Introduction

3 NLP Applications

4 NLP Techniques


6 Challenges

7 Course Materials


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NLP Course

LectureThursdays 9:15-10:45Location: HS 3Credit Point: 3 LP

AssessmentRegular attendance in the classDoing exercisesFinal exam

ContactSaeedeh Momtazi

Email: [email protected]: A-1.7


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[email protected]

NLP Course

Course Home Pagehttp://www.hpi.uni-potsdam.de/naumann/teaching/ss_12/natural_language_processing.html

Administrative informationSlidesExercises

Mailing ListWill be set later


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http://www.hpi.uni-potsdam.de/naumann/teaching/ss_12/natural_language_processing.html

http://www.hpi.uni-potsdam.de/naumann/teaching/ss_12/natural_language_processing.html

Outline


2 Introduction

3 NLP Applications

4 NLP Techniques


6 Challenges

7 Course Materials


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Different Types of Languages

Natural languagesEnglishGermanFrenchSpanish...

Formal languagesJavaPythonLaTeX...

Descriptive languagesBiology: DNAChemistry: chemical formulas...


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Natural Language

A vocabulary consists of a set of words (wi )

A text is composed of a sequence of words from the vocabulary

A language is constructed of a set of all possible texts


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Natural Language

Examples of Vocabularies

Englishtheandeatyoubook...

Germandasundessendubuch...


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Outline1 Administrative Information

2 Introduction

3 NLP ApplicationsText TechnologiesSpeech Technologies

4 NLP Techniques


6 Challenges

7 Course Materials


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Spell and Grammar Checking

Checking the spelling and the grammar of a text,and suggesting correct alternatives for the errors

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Spell and Grammar Checking


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Text Categorization

Assigning each text to a category

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Text Categorization


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Information Retrieval

Finding relevant information to the user’s query

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Information Retrieval


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Summarization

Finding the most relevant part of a document based on theuser’s information need

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Summarization


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Information Extraction

Extracting the important items of a text and structuring them

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Question Answering

Answering natural language questions asked by the user

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Question Answering


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Question Answering

Crete


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Machine Translation

Translating a text from one language to another language

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Machine Translation


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Data Fusion

Combining extracted information from several text files into adatabase or an ontology

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Data Fusion


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Sentiment Analysis

Identifying positive and negative opinions stated in a text

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Sentiment Analysis


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Optical Character Recognition

Recognizing printed or handwritten texts and converting themto computer-readable texts

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Optical Character Recognition


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Word Prediction

Predicting the next word that is highly probable to be typed bythe user

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Word Prediction


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Outline1 Administrative Information

2 Introduction

3 NLP ApplicationsText TechnologiesSpeech Technologies

4 NLP Techniques


6 Challenges

7 Course Materials


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Speech Recognition

Recognizing a spoken language and transforming it into a text

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Speech Recognition


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Speech Synthesis

Producing a spoken language from a text

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Speech Synthesis


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Spoken Dialog Systems

Running a dialog between the user and the system


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Spoken Dialog Systems


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Applications’ Levels

Easy (mostly solved)Spell and grammar checkingSpam detection

Intermediate (good progress)Information retrievalSentiment analysisMachine translationInformation extraction

Difficult (still hard)Question answeringSummarizationDialog system


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Outline


2 Introduction

3 NLP Applications

4 NLP Techniques


6 Challenges

7 Course Materials


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Part Of Speech Tagging

“I saw the man on the roof.”

“ I[PRON] saw[V ] the[DET ] man[N] on[PREP] the[DET ] roof[N]. ”

[PRON] Pronoun[PREP] Preposition[DET] Determiner[V] Verb[N] Noun


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Parsing

“I saw the man on the roof.”


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Named Entity Recognition

“Steven Paul Jobs, co-founder of Apple Inc, was born in California.”

“ Steven Paul JobsPerson

, co-founder of Apple IncOrganization

, was born in CaliforniaLocation

.”


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Word Sense Disambiguation

“Jim flew his plane to Texas.”

“Alice destroys the item with a plane.”


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Semantic Role Labeling

“John grills a fish on an open fire.”

CookFood

Heating-Instrument

“ JohnCook

grills a fishFood

on an open fireHeating−Instrument

.”


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Outline


2 Introduction

3 NLP Applications

4 NLP Techniques


6 Challenges

7 Course Materials


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Linguistic Knowledge

Phonetics and PhonologyMorphologySyntaxSemanticsPragmaticsDiscourse


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Phonetics and Phonology

The study of linguistic sounds and their relations to words


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Morphology

The study of internal structures of words and how they can bemodified

Parsing complex words into their components

unbelievableun+believ+able

prefix: unroot: believesufix: able


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Syntax

The study of the structural relationships between words in asentence

I saw the man on the roof.


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Semantics

The study of the meaning of words, and how these combine toform the meanings of sentences

Realizing lexical relations among words

Hyponymy (is a) dog & animal

Meronymy (part of) arm & body

Synonymy fall & autumn

Antonymy tall & short


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Pragmatics

The study of how language is used to accomplish goals, andthe influence of context on meaning

Understanding the aspects of a language which depends onsituation and world knowledge

Do you know what time it is?


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Discourse

The study of linguistic units larger than a single utterance

John reads a book. He borrowed it from his friend.


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Outline


2 Introduction

3 NLP Applications

4 NLP Techniques


6 Challenges

7 Course Materials


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Challenges

Different words/sentences express the same meaningThe third season of the year

FallAutumn

Book delivery timeWhen will my book arrive?When will I receive my book?

One word/sentence can have different meaningsFall

The third season of the yearMoving down towards the ground or towards a lower position

The door is open.Expressing a factA request to close the door

Paraphrasing

Ambiguity


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Challenges

Moving down?The third season of the year?

What about autumn?


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Phonetics and Phonology

Computers can recognize speech

Computers can wreck a nice peach


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Morphology

Unionize

Union + ize

Un + ion + ize


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Syntax

I saw the man with a telescope.


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Syntax

I saw the man with a telescope.


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Semantics

The astronomer loves the star.

Movie Star (Celebrity)

Sky Star


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Semantics

Every man loves a woman.

∀x : (man(x) → ∃y : (woman(y) & love(x , y)))

∃y : (woman(y) & ∀x : (man(x) → love(x , y)))


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Pragmatics

Can you give me the salt?

Would you please give me the salt?

Do you have the ability to give me the salt?


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Discourse

Alice understands that you like your mother, but she ...

Alice

Your Mother


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Ambiguity

I made her duck.


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Outline


2 Introduction

3 NLP Applications

4 NLP Techniques


6 Challenges

7 Course Materials


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Topics

IntroductionIntroduction to Language TechnologyLanguage Modeling

Machine Learning for NLP

NLP Techniques

NLP Applications


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Topics

Introduction

Machine Learning for NLPLearning TechniquesClassification AlgorithmsClustering Algorithms

NLP Techniques

NLP Applications


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Topics

Introduction


NLP TechniquesPart Of Speech TaggingParsingNamed Entity RecognitionWord RelationsWord Sense DisambiguationSemantic Role Labeling

NLP Applications


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Topics

Introduction


NLP Techniques

NLP ApplicationsText CategorizationInformation RetrievalInformation ExtractionQuestion AnsweringSentiment AnalysisSummarizationMachine Translation


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Course Book

SPEECH AND LANGUAGE PROCESSING

An Introduction to Natural Language Processing,Computational Linguistics, and Speech Recognition

by Daniel Jurafsky and James H. Martin

Second Edition


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Relevance Journal & Conferences

Journal

Computational Linguistics

Conferences

ACL: Association for Computational LinguisticsNAACL: North American ChapterEACL: European Chapter

HLT: Human Language Technology

EMNLP: Empirical Methods on Natural Language Processing

CoLing: Computational Linguistics

LREC: Language Resources and Evaluation


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