Natural Language Processing >>Introduction

Natural Language Processing

>>Introduction<<

winter / fall 2017/2018

41.4872

Prof. Dr. Bettina Harriehausen-Mühlbauer

Univ. of Applied Science, Darmstadt, Germany

https://www.fbi.h-da.de/organisation/personen/harriehausen-muehlbauer-bettina.html

[email protected]








mailto:[email protected]



Natural Language Processing 2

What does Startreck have to do with NLP ?

the past - the present - the future

http://www.youtube.com/watch?v=LkqiDu1BQXY

The Voyage Home

../WS 12-13/NLP/Startreck-1.avi

http://www.youtube.com/watch?v=LkqiDu1BQXY


Applications in our daily life



Hi Google…


Do you remember the story of the little girl that wore a red cape and which met a wolf while going to her grandmother‘s house ?


Little Red Ridinghood

Rotkäppchen

Le Petit Chaperon Rouge


Do you remember the story of the little girl that wore a red cape and which met a wolf while going to her grandmother‘s house ?


keywords vs. stopwords

• Relevant questions:

How to retrieve relevant texts

How to understand written / spoken texts

How to answer questions

How to translate texts between languages

How to generate written / spoken texts

How to summarize texts

How to tell a story

NLP-Applications in our daily life and … what we expect machines to (be able to) do

… and what NLP developers currently focus on


NLP (& AI) today:

relevant and ubiquitous

• Unnoticed (and often not under the term „AI“),

NLP/AI applications are ubiquitous in everyday life:

– NLP: Speech control in cars, mobile phones, etc.

– AI: Face recognition in cameras

– NLP: (Learning) Spam classification in e-mail clients

– AI: in computer games

– NLP: Semantic search of the Internet

– NLP: Machine translation services in the Internet

• Professional AI applications:

– Business intelligence, sentiment analysis

– Robotics

– Military applications: drones, …

Image Source: Wikimedia



What is NLP / Computational Linguistics ?


What is NLP / Computational Linguistics ?

definition:

A system is called a natural language processing system when

• a subset of the input or output of the system is coded / written in a natural language and

• the processing of the data is performed by algorithms for the morpho-syntactic, semantic, and pragmatic analysis or generation of natural language


Natural Language Processing is ...

an interdisciplinary field / art / science

• computer science (NLP = a subfield of A.I.)

• linguistics (language independent)

• mathematics (logics, predicate logic, theroem

proving, knowledge based systems, statistics, ...)

• physics (speech recognition, spoken language)

• ...

Alan Turing

„Turing test“

John McCarthy

- invented LISP

Marvin Minsky

Founder of „A.I.“

Noam

Chomsky

- formalizing

(natural)

language

- generative

grammar

Lotfi Zadeh

„Fuzzy Logic“ Robert Wilensky

- LISP

- text understanding

Joseph Weizenbaum

- ELIZA


Natural Language Processing is ...

a broad field / art / science

• phonetics / phonology (speech processing / speech recognition)

phonemes = the smallest meaning-distinguishing items

• morphology (segmentation , compounding,...) - tokenization

morphemes = the smallest items carrying meaning

• lexicology / electronic dictionaries – tagging

lexemes , lemmas vs. full-forms (each entry needs a tag)

idiomatic expressions , neologisms / „trendy words“ , homonyms , …

• syntax (analysis and generation of phonemes, morphemes, lexemes,

phrases, sentences, paragraphs) …grammar / - formalisms

(from transformation to unification)

• semantics (meaning, disambiguation, anaphora resolution,...)

• pragmatics (discourse representation)


We will focus on... (1)

• intro

• morphology

• parsing / tokenization

• compounds

• lexicon / electronic dictionaries

• lemmas / inflected forms

• coding features / tagging

• idiomatic expressions

• neologisms / „trendy words“

• homonyms



• syntax -> semantics : from transformation to unification (RTN / ATN), case grammar (Fillmore) , CD-structures

• machine translation



dictionary grammar

parser

=> rule-based NLP (as opposed to statistical NLP)



• get an overview of and understand the scope of NLP

• get an overview of the state-of-the-art technologies (subset)

• understand the parallels between CL / NLP and A.I.

• reach the ability to use principles of linguistic theories in NLP programming

• develop a (prototype) of an NLP system (= project task)

together, we want to:


reading material (obligatory)

Latest edition: Prentice Hall, 2008

ISBN-10: 0131873210, ISBN-13: 978-0131873216

First chapter: http://www.cs.colorado.edu/~martin/SLP/Updates/1.pdf

http://www.cs.colorado.edu/~martin/SLP/Updates/1.pdf


reading material (recommended)

MIT Press, 1999, ISBN 0262133601

Reader link: http://www.amazon.de/gp/reader/0262133601/ref=sib_dp_pt/028-2523061-0018166#reader-page

http://cognet.mit.edu/library/books/view?isbn=0262133601

http://www.amazon.de/gp/reader/0262133601/ref=sib_dp_pt/028-2523061-0018166#reader-page








http://cognet.mit.edu/library/books/view?isbn=0262133601



Pierre M. Nugues

An Introduction to Language Processing with Perl and Prolog: An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German (Cognitive Technologies). Springer Berlin Heidelberg.

ISBN-10: 3642064051

ISBN-13: 978-3642064050

Latest edition: 2010

Reader link: http://www.amazon.de/Introduction-Language-Processing-Perl-Prolog/dp/3642064051/ref=sr_1_2?s=books-intl-de&ie=UTF8&qid=1316528179&sr=1-2#reader_3642064051

http://www.amazon.de/Introduction-Language-Processing-Perl-Prolog/dp/3642064051/ref=sr_1_2?s=books-intl-de&ie=UTF8&qid=1316528179&sr=1-2#reader_3642064051
















• Natural Language Processing with Python von Julie Steele, Robert

Romano, Steven Bird und Ewan Klein von O'Reilly Media (7. Juli 2009)

•

• Available as download.


http://www.amazon.de/Natural-Language-Processing-Python-Steele/dp/0596516495/ref=sr_1_1?s=books-intl-de&ie=UTF8&qid=1359648708&sr=1-1


• Machine Translation: Its Scope and

Limits (Studies in Natural Language

Processing) by Yorick Wilks.

Cambridge University Press (1st ed.

2006)


http://www.amazon.de/Machine-Translation-Studies-Language-Processing/dp/0521643678/ref=sr_1_8?s=books-intl-de&ie=UTF8&qid=1359648708&sr=1-8












more...reading material (A.I.)

• Bobrow, D.G., Winograd, T. „An Overview of KRL, a Knowledge Representation

Language“ in: Cognitive Science, Vol.1, No.1, 3-46, 1977

• Charniak, E. „A common representation for problem solving and natural language

comprehension information“. Artificial Intelligence, 1981, 225-255.

• Friedman, J.A. Computer Model of Transformational Grammar. New York:

Elsevier. 1971.

• Christopher D. Manning (Author), Prabhakar Raghavan (Author), Hinrich Schütze

(Author). Introduction to Information Retrieval. Cambridge University Press.

2008. ISBN-10: 0521865719 ISBN-13: 978-0521865715

• Norvig, Peter. Unified Theory of Inference for Text Understanding. Univ. of

California, Berkeley, Computer Science Division. Report. No. UCB/CSD 87/339.

1987.

• Quillian, M.R. „Sematic Memory“. In: M.Minsky, ed. Semantic Information

Processing. MIT Press. Cambridge. 1968. more

http://www.amazon.com/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Christopher%20D.%20Manning

http://www.amazon.com/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Prabhakar%20Raghavan

http://www.amazon.com/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Hinrich%20Sch%C3%BCtze


more...reading material (A.I.)

• Stuart Russell (Author), Peter Norvig (Author) Artificial Intelligence: A Modern Approach (2nd Edition) (Prentice Hall Series in Artificial Intelligence). Prentice Hall, 2002. ISBN-10: 0137903952

• Schank, R.C. Conceptual Information Processing. Amsterdam: North Holland. 1975.

• Schank, R.C., Abelson, R.P. Scripts, Goals and Understanding: An Inquiry into Human Knowledge Structures. Hillsdale: Lawrence Erlbaum Associates. 1977.

• Wilensky, R., Arens, Y. PHRAN: A knowledge-based approach to natural language analysis. Electronics Research Laboratory, College of Engineering. University of California, Berkeley. Memorandum No. UCB/ERL M80/34. 1980.

• Wilensky, Robert. „Some Problems for proposals for Knowledge Representation“. University of Berkeley, CS Dept. 1986.

• Woods, W.A. „What‘s a link: Foundations for Semantic Networks“. In: Representation and Understanding: Studies in Cognitive Science. D.G. Bobrow, A. Collins, eds. New York: Academic Press, 1975.

more

http://www.amazon.com/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Stuart%20Russell

http://www.amazon.com/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Peter%20Norvig


more...reading material (NLP)

• Bresnan, Joan, ed. The mental Representations of Language. London: MIT Press. 1982.

• Bresnan, Joan. Lexical Functional Grammar. Stanford Linguistic Institute. 1987

• Chomsky, Noam. Aspects of the Theory of Syntax. Cambridge: MIT Press. 1965.

• Ronen Feldman (Author), James Sanger (Author) The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data (Hardcover). Cambridge University Press. 2006.

• Fillmore, Charles. The Case for Case. Ohio State University, 1968.

• Fillmore, Charles. „The case for Case reopened“. In: P. Cole, J.M. Saddock, eds. Syntax and Semantics 8: Grammatical Relations. Academic Press, N.Y. 1977.

• Harriehausen, B. „Why grammars need to expand their scope of parsable input“, Proceedings Second Conference on Arabic Computational Linguistics, Kuwait, 11/89.

• Harriehausen, B. „The PLNLP Grammar checkers - CRITIQUE“, Proceedings ALLC-ACH 90 Conference „The New Medium“. Siegen. 6/1990.

• Harriehausen-Mühlbauer, B. „PLNLP - a comprehensive natural language processing system for analysis and generation across languages“, Proceedings: The First International Seminar on Arabic Computational Linguistics, Egyptian Computer Society, Cairo, 6/92.

more

http://www.amazon.com/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Ronen%20Feldman

http://www.amazon.com/exec/obidos/search-handle-url?%5Fencoding=UTF8&search-type=ss&index=books&field-author=James%20Sanger


more...reading material (NLP)

• Harriehausen-Mühlbauer, B,. Koop, A. „SCRIPT - a prototype for the recognition of continuous, cursive, handwritten input by means of a neural network simulator“, Proceedings 1993 IEEE International Conference on Neural Networks, San Francisco, 3/1993.

Jurafsky, Daniel, and James H. Martin. 2008. Speech and Language Processing: An Introduction to Natural Language Processing, Speech Recognition, and Computational Linguistics. 2nd edition. Prentice-Hall.

Manning, Christopher / Schütze, Hinrich. Foundations of Statistical Natural Language Processing. MIT Press. 1999.

• Levin, L., Rappaport, M., Zaenen, A., eds. Papers in Lexical Functional Grammar. Bloomington: Indiana University Linguistics Club. 1983.

• Ruslan Mitkov (Editor) The Oxford Handbook of Computational Linguistics (Oxford Handbooks in Linguistics). Oxford University Press. 2005 .

• Radford, A. Transformational Syntax. Cambridge: Cambridge University Press. 1981.

• Rieger, C.J. „Conceptual Memory and Inference“. In: R.C. Schank. Conceptual Information Processing. North Holland. 1975.

• Shieber, S.M. An Introduction to Unification-based Approaches to Grammar. Stanford: CSLI. 1986.

• Winograd, T. Phenomenological Foundations of AI in Language.Stanford University, Linguistic Institute, 1987.


history of NLP / CL

How did it all start ?

1949-1960 beginning of electronic language processing: machine translation, linguistics data processing

The spirit is strong but the flesh is weak.

->

The vodka is strong but the meat is rotten.


history of NLP / CL


1960-1970 first formal (transformation) grammars (Chomsky 1957), beginning of language oriented research in A.I.: first simple question-answering- systems; keyword (pattern-matching)- systems

1963 Sad-Sam (Lindsay), BASEBALL (Green)

1966 DEACON (Craig), ELIZA (Weizenbaum), SYNTHEX (Simmons et.al.)

1968 TLC (Quillian), SIR (Raphael), STUDENT (Bobrow), CONVERSE (Kellog)


ELIZA – pattern-matching (1)

• ELIZA is a computer program devised by Joseph Weizenbaum (1966) that simulates the role of a Rogerian psychologist.

• ELIZA was one of the first programs developed that explored the issues involved in using natural language as the mode of communication between humans and the machine. Source:

http://www.publications.parliament.uk/pa/cm201012/cmselect/cms

ctech/1537/1537vw03.gif

http://www.publications.parliament.uk/pa/cm201012/cmselect/cmsctech/1537/1537vw03.gif

http://www.publications.parliament.uk/pa/cm201012/cmselect/cmsctech/1537/1537vw03.gif



Why Simulate a Rogerian Psychologist?

Client-Centered Therapy (CCT), was developed by Carl Rogers in the 40's and 50's and is described as being a "non-directive" approach to counselling. That is, unlike most other forms of counselling, the therapist does not offer treatment, disagree, point out contradictions, or make interpretations or diagnoses. Instead, CCT is founded on the belief that people have the capacity to figure out their own solutions which can be facilitated by a psychologist who provides an accepting and understanding environment. As pointed out by Weizenbaum, "[this form of] psychiatric interview is one of the few examples of categorized dyadic natural language communication in which one of the participating pair is free to assume the pose of knowing almost nothing of the real world." For example, an appropriate response to a client's comment of "I went for a long walk„ could possibly be "Tell me about long walks." In this reply, the client would not assume that the therapist knew nothing about long walks, but instead, had some motive for steering the conversation in this direction. Such assumptions make this an appealing domain to simulate, as a degree of realism can be obtained without the need for storing explicit information about the real world.



How successful is ELIZA ?



How does ELIZA work?

• identifying keywords or phrases that the user inputs

• using patterns associated with these phrases to generate responses

• the most basic of these output patterns respond identically to all sentences containing the keyword




key: xnone 0

answer: I‘m not sure I understand you fully-

answer: That is interesting. Please continue.

key: sorry

answer: Please don‘t apologise.

answer: Apologies are not necessary.

xnone = ELIZA responds to an input sentence that is not understood

(xnone is the default used when no other keyword is found in the sentence)

sorry = ELIZA responds to an input sentence that contains the word „sorry“

single keywords triggering a response:




key: I like xxx. (where xxx is an arbitrary string)

answer: Why do you like xxx ?

answer: Why do you say you like xxx ?

keyphrases triggering a response with a conversion:

Example:

user: I like xxx.

ELIZA: Why do you like xxx?




key: I am xxx. (where xxx is an arbitrary string)

answer: Tell me why you think you are xxx .

keyphrases triggering a response with a conversion:

Example:

user: I am very unhappy at the moment.

ELIZA: Tell me why you think you are very unhappy at the moment.




key: remember

decomp: * I remember *

answer: Do you often think of (2) ?

answer: What else do you recollect ?

keyphrases triggering a response with a conversion plus postprocessing of reference words:

Example:

user: I remember my first boyfriend.

Decomposition: the first * = empty string, the second * = my first boyfriend (= (2))

ELIZA: Do you often think of (* my ) your first boyfriend.



Now it‘s your turn ! (1st assignment)

Try out ELIZA, make up your own mind as to ELIZA‘s realism. Get a first idea of man-machine communication.



to „play“ with ELIZA (see: the following links)

ELIZA program:

http://www.manifestation.com/neurotoys/eliza.php3

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

Reading:

Joseph Weizenbaum: ELIZA - A Computer Program For the Study of

Natural Language Communication Between Man And Machine. in:

Communications of the ACM. New York 9.1966,1. ISSN 0001-0782

http://www.manifestation.com/neurotoys/eliza.php3






but now back to the history of NLP / CL


history of NLP / CL


1970-1980 knowledge-based expert systems and natural language database interfaces, development of formal grammars (esp. syntax analysis) dialogue systems 1972 SHRDLU (Winograd) 1977 GUS (Bobrow et.al.), PAL (Sidner et.al.) natural language interfaces 1972 LUNAR (Woods et.al.) 1972-1976 RENDEVOUZ (Codd), REL (Thompson), REQUEST (Plath) 1977 LIFER (Henrix), INTELLECT (Harris), PLANES (Waltz et.al.), CO-OP (Kaplan)


SHRDLU Person: PICK UP A BIG RED BLOCK.

Computer: OK. (does it)

Person: GRASP THE PYRAMID.

Computer: I DON'T UNDERSTAND WHICH PYRAMID YOU MEAN.

Person: FIND A BLOCK WHICH IS TALLER THAN THE ONE YOU ARE HOLDING AND

PUT IT INTO THE BOX.

Computer: BY "IT", I ASSUME YOU MEAN THE BLOCK WHICH IS TALLER THAN THE

ONE I AM HOLDING.

Computer: OK. (does it)

Person: WHAT DOES THE BOX CONTAIN?

Computer: THE BLUE PYRAMID AND THE BLUE BLOCK.

Person: WHAT IS THE PYRAMID SUPPORTED BY?

Computer: THE BOX.

Person: HOW MANY BLOCKS ARE NOT IN THE BOX?

Computer: FOUR OF THEM.

Person: IS AT LEAST ONE OF THEM NARROWER THAN THE ONE WHICH I TOLD YOU

TO PICK UP?

Computer: YES, THE RED CUBE.

Person: IS IT SUPPORTED?

Computer: YES, BY THE TABLE.


Natural language DB interface

LanguageAccess (natural language interface to a relational database)

- A product by IBM (~ early 90ies)

Sentence xy: WHICH COUNTRY EXPORTS FISH

SQL-query: SELECT DISTINCT X1 COUNTRY, X1.PRODUCT

FROM EXPORTBASE X1

WHERE X1.PCLASS=„FMF“


Natural language DB interface

=> which – of course – is different to an internet search


history of NLP / CL

How did it all start ? text “understanding“ and text generating systems

1975 MARGIE (Schank et.al.), SAM (Schank et.al.) 1976-1979 TALE-SPIN (Meehan), PAM (Wilensky), FRUMP (DeJong)

1980 PHRAN (Wilensky)

• 1980-1990 focus on semantic-pragmatic analysis, natural language applications, models of complex communication pattern

- robust dialogue systems

- integration of natural language components in expert systems

- knowledge acquisition via natural language (both man and machine learn)


history of NLP / CL


• 1990-2000+ machine translation (revival), data mining / text mining, intelligent text processing systems (text critiquing), integration of computerlinguistic components in multimedia (CALL, CBT,...), SIRI, ...

boom (integration of NLP everywhere)

that‘s where we are today:

- growing demand

- growing size of applications

- growing user expectations


NLP / CL today

we have come very far,

but... ...there are still a lot of open questions:

• what is knowledge ?

• when do we have to consider knowledge in natural language processing ?

• how can knowledge be formalized ?

• how are the analysis of language and the understanding of language interrelated ?

• what is communication ?

• easy (?) natural language

• technical language as a „dialect“ of natural language (e.g. medical language)

• artificial language as „meta language“ (e.g. Esperanto)

• logics (a special form of representation on an abstract level)


Question : Natural language ... easy ?

…after all… we all use / speak / write it

Does this mean natural language is easy and

easy to formalize ?


Natural language ... easy ?

(counter) example: natural language database query

LanguageAccess (natural language interface to a relational database)

Sentence : WHICH COUNTRY EXPORTS FISH

SQL-query: SELECT DISTINCT X1 COUNTRY, X1.PRODUCT

FROM EXPORTBASE X1

WHERE X1.PCLASS=„FMF“

natural language paraphrase / disambiguation of the input:

Which interpretation did you mean ?

Which country exports the product fish (fish = object)

Which country is exported by fish (fish=subject)

in German: with zero-article, it‘s ambiguous (disambiguation by case marking of the article)



language is extremely ambiguous

easy for humans ??? easy for machines ???

• lexical The pipe was brandnew.

• structural I saw the man with the telescope.

• deep structural She got ready for the picture.

• semantic Mary wants to get married to an Italian.

• pragmatic While walking from the gate to the house it collapsed.



language is complex...you can say a lot with a few words

Mary sold John a book.

surface structure (obvious): transfer of book

deep structure (implication of „to sell“): transfer of money



language is complex…but some things are still „missing“ (here: semantic gaps); it‘s not always logical

ENGLISH

male female neutral

father mother parent

brother sister sibling

uncle aunt

nephew niece

cousin

GERMAN

male female neutral

Vater Mutter Eltern

Bruder Schwester (Geschwister)

Onkel Tante

Neffe Nichte

Cousin/Vetter Cousine/Base

Exceptions need to be learned

Mistakes are made due to overgeneralization: „he catched a fish“, „informations“

Examples across language (German): Obst, Fleisch, Schnee; (English): furniture

(have no plural form)

other words have no singular form : Möbel (German)



language is analyzed on different levels


The 7 levels of language understanding

Needed knowledge:

features of the voice phonetic analysis

sound combinations of language phonological analysis

dictionary morphological / lexical analysis

grammar rules (parser) syntactic analysis

knowledge representation semantic analysis

world knowledge pragmatic analysis

acoustic signals

Bill ... letters

Billy is eating his lunch. sentences

small (Billy) knowledge

Billy... words

[Billy] is [mother] consequences child of

æ ç Þ ð ţ ş sounds



Why then natural language ?

Computers speak their own language. This language is efficient, economical, and exact. Why then would we want to „teach“ the computer a natural language with all its ambiguities and difficulties ?

Back to the boom!

when busy with your hands and you still want to type (voice type)

when you are a slow typer (voice type)

when travelling (machine translation)

when you don‘t want to learn a programming language to program your computer (machine translation)

when you want to evaluate millions of lines of text (text/data mining)

when you don‘t want to learn a database query language to get data (Startrek) (textanalysis, textgeneration, machine translation)

when you need to make a phonecall with someone in Japan, but you don‘t speak Japanese (voice recognition, machine translation)


applications of natural language

systems

spoken text written text

Speech input Speech output

Dialogue Systems

dialogue understanding

text

analysis generation

translation

• speaker & voice recognition

• spoken commands / command & control

• automatic dictation

• text-to-speech

• telephony

• IVR (interactive voice response)

• information systems

• DB query

• expert systems

• CALL

• robot stearing

• programming languages

• spell aid

• text critiquing

• text summaries

• knowledge acquisition (e.g. for expert systems)

• help functions for translations

• automatic translation

•simultaneous translation

• explanations for users

• knowledge representation

• text generation

• writing support

Applications

Natural Language Processing >>Introduction

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