introduction to language technology potsdam, 12 april 2012 · introduction to language technology...
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Natural Language ProcessingIntroduction to Language Technology
Potsdam, 12 April 2012
Saeedeh MomtaziInformation Systems Group
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Outline
1 Administrative Information
2 Introduction
3 NLP Applications
4 NLP Techniques
5 Linguistic Knowledge
6 Challenges
7 Course Materials
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Outline
1 Administrative Information
2 Introduction
3 NLP Applications
4 NLP Techniques
5 Linguistic Knowledge
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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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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Outline
1 Administrative Information
2 Introduction
3 NLP Applications
4 NLP Techniques
5 Linguistic Knowledge
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
5 Linguistic Knowledge
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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Information Extraction
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Information Extraction
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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
5 Linguistic Knowledge
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
1 Administrative Information
2 Introduction
3 NLP Applications
4 NLP Techniques
5 Linguistic Knowledge
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
1 Administrative Information
2 Introduction
3 NLP Applications
4 NLP Techniques
5 Linguistic Knowledge
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
1 Administrative Information
2 Introduction
3 NLP Applications
4 NLP Techniques
5 Linguistic Knowledge
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
1 Administrative Information
2 Introduction
3 NLP Applications
4 NLP Techniques
5 Linguistic Knowledge
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
Machine Learning for NLP
NLP TechniquesPart Of Speech TaggingParsingNamed Entity RecognitionWord RelationsWord Sense DisambiguationSemantic Role Labeling
NLP Applications
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Topics
Introduction
Machine Learning for NLP
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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