Media Technology 4 - Advanced I/O-Devices

1
"Media Technology 4 - Advanced I/O-Devices"

• Man-Machine Interfaces (MMI)

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Man-Machine-Inferfaces

• Introduction
• To work with a system, users have to be able to
  control the system and assess the state of the
  system. E.g., driving an automobile
• The user interface of the automobile is on the
  whole composed of the instruments the driver can
  use to accomplish the tasks of driving and
  maintaining the automobile.

3

• http//www.gemo-netz.de/rostock/diesunddas/Luftwaf
  fe_Laage_2006/img/C-160_Transall_Cockpit.jpg

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Man-Machine-Inferfaces

• User - Usability
• The design of a user interface affects the
  amount of effort the user must spend to provide
  input for the system and to interpret the output
  of the system, and how much effort it takes to
  learn how to do this i.e. tghis should be
  intuitive
• Usability is the degree to which the design of a
  particular user interface takes into account the
  human psychology and physiology of the users, and
  makes the process of using the system effective,
  efficient and satisfying. (keyword ergonomics)
• Usability is mainly a characteristic of the
  user interface, but is also associated with the
  functionalities of the product and the process to
  design it. It describes how well a product can be
  used for its intended purpose by its target users
  with efficiency, effectiveness, and satisfaction,
  also taking into account the requirements from
  its context of use. (the user is of key
  importance)

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Man-Machine-Inferfaces

• User - Usability
• Usability is a term used to denote the ease with
  which people can employ a particular tool or
  other human-made object in order to achieve a
  particular goal.
• Usability can also refer to the methods of
  measuring usability (usability tests) and the
  study of the principles behind an object's
  perceived efficiency or elegance.
• In human-computer interaction and computer
  science, usability usually refers to the elegance
  and clarity with which the interaction with a
  computer program or a web site is designed. It
  can also refer to the efficient design of
  mechanical objects such as a door handle or a
  hammer.
• 'User-friendly' is one of the more popular
  buzz-words currently in vogue within the
  information-processing community. Everyone pays
  lip service to the idea that building
  user-friendly systems is a good thing, but people
  are a little more vague about what this really
  means and how it can be accomplished.

!
6
Man-Machine-Inferfaces

• Terminology
• user interface
• Man (user) Machine- Interface
• Human-Machine Interface (HMI)
• Human-computer interaction / Human-computer
  interface (HCI)
• Operator Interface Console (OIC)
• Operator Interface Terminal (OIT)
• Man-Machine-Communication

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Man-Machine-Inferfaces

• Special areas / applications
• Direct neural interfaceIn science fiction, a
  MMI is sometimes used to refer to what is better
  described as direct neural interface. This usage
  is seeing increasing application in the real-life
  use of (medical) prosthesesthe artificial
  extension that replaces a missing body part.
• Immersive interfacesComputers observe the user,
  and react according to their actions without
  specific commands. A means of tracking parts of
  the body is required, and sensors noting the
  position of the head, direction of gaze and so on
  have been used experimentally.

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Man-Machine-Inferfaces

• Definition
• The user interface (also known as Human Computer
  Interface or Man-Machine Interface (MMI)) is the
  tool or the sum of means by which peoplethe
  usersinteract with the systema particular
  machine, device, computer program or other
  complex tool. The user interface provides means
  of
• Input, allowing the users to manipulate a system
• Output, allowing the system to indicate the
  effects of the users' manipulation.

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Man-Machine-Inferfaces

• Only one interface per system ?(think about a
  traffic light)

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Man-Machine-Inferfaces

• Only one interface per system ?
• A system may expose several user interfaces to
  serve different kinds of users.
• For example, an application might provide two
  user interfaces, one for common endusers (limited
  set of functions, optimized for ease of use) and
  the other for trained personnel (wide set of
  functions, optimized for efficiency) e.g. in a
  bank.

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Man-Machine-Inferfaces

• In computer science and human-computer
  interaction, the user interface (of a computer
  program) refers to the graphical, textual and
  auditory information the program presents to the
  user, and the control sequences (such as
  keystrokes with the computer keyboard, movements
  of the computer mouse, and selections with the
  touchscreen) the user employs to control the
  program.

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Man-Machine-Inferfaces

• Types of Interfaces
• Currently (as of 2009) the following types of
  user interfaces are the most common
• Graphical user interfaces (GUI) accept input via
  devices such as computer keyboard and mouse and
  provide articulated graphical output on the
  computer monitor.
• Web-based user interfaces or web user interfaces
  (WUI) accept input and provide output by
  generating web pages which are transmitted via
  the Internet and viewed by the user using a web
  browser program. Newer implementations utilize
  Java, AJAX, Adobe Flex, Microsoft .NET, or
  similar technologies to provide real-time control
  in a separate program, eliminating the need to
  refresh a traditional HTML based web browser.
  Administrative web interfaces for web-servers,
  servers and networked computers are often called
  Control panels.

13
Man-Machine-Inferfaces

• User interfaces that are common in various fields
  outside
• desktop computing
• Command line interfaces, where the user provides
  the input by typing a command string with the
  computer keyboard and the system provides output
  by printing text on the computer monitor. Used
  for system administration tasks etc.
• Touch interfaces are graphical user interfaces
  using a touchscreen display as a combined input
  and output device. Used in many types of point of
  sale, industrial processes and machines,
  self-service machines etc.

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Man-Machine-Inferfaces

• History
• The history of user interfaces can be divided
  into the
• following phases according to the dominant type
  of user
• interface
• Batch interface, 1945-1968
• Command-line user interface, 1969 to Graphical
  user interface, 1981 to present
• Tangible interfaces / Ubicomp
• Touch User Interface (TUI), e.g. iPhone

15
Man-Machine-Inferfaces

• Samples
• Small and robust hi-fidelity tactile
  transducer with USB interfaces for man/machine
  interface applications
• A Norwegian company has developed a novel
  computer interface,
• Creating realistic multi-dimensional tactile
  feedback. The device
• enables a dramatic
  enhancement of the human-
  machine interface for the mass
  market. The compact,
  robust unit is the only device on the
• market with real 2
  dimensional X-Y capability and
  hi-fidelity tactile bandwidth.Electronics
  with a standard USB
  interface, drivers and
  support/development SW is available. Industrial
  partners for
  licensing/technical cooperation are
  sought.
• http//www.ircnet.l
  u/src/request/pictures/Compubilde.jpg

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Man-Machine-Inferfaces

• Samples The interface controls division of
  DeltaTech Controls is specialising in the design
  and manufacture of custom and bespoke joystick,
  footpedal, analogue rocker and armrest products,
  primarily targeted at the industrial vehicle
  market - has been renamed as.

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Man-Machine-Inferfaces

• Other types of user interfaces (1/3)
• Attentive user interfaces manage the user
  attention deciding when to interrupt the user,
  the kind of warnings, and the level of detail of
  the messages presented to the user.
• Batch interfaces are non-interactive user
  interfaces, where the user specifies all the
  details of the batch job in advance to batch
  processing, and receives the output when all the
  processing is done. The computer does not prompt
  for further input after the processing has
  started.
• Conversational Interface Agents attempt to
  personify the computer interface in the form of
  an animated person, robot, or other character
  (such as Microsoft's Clippy the paperclip), and
  present interactions in a conversational form.
• Crossing-based interfaces are graphical user
  interfaces in which the primary task consists in
  crossing boundaries instead of pointing.
• Gesture interfaces are graphical user interfaces
  which accept input in a form of hand gestures, or
  mouse gestures sketched with a computer mouse or
  a stylus.

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Man-Machine-Inferfaces

• Other types of user interfaces (2/3)
• Intelligent user interfaces are human-machine
  interfaces that aim to improve the efficiency,
  effectiveness, and naturalness of human-machine
  interaction by representing, reasoning, and
  acting on models of the user, domain, task,
  discourse, and media (e.g., graphics, natural
  language, gesture).
• Multi-screen interfaces, employ multiple displays
  to provide a more flexible interaction. This is
  often employed in computer game interaction in
  both the commercial arcades and more recently the
  handheld markets.
• Noncommand user interfaces, which observe the
  user to infer his / her needs and intentions,
  without requiring that he / she formulate
  explicit commands.
• Reflexive user interfaces where the users control
  and redefine the entire system via the user
  interface alone, for instance to change its
  command verbs. Typically this is only possible
  with very rich graphic user interfaces.

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Man-Machine-Inferfaces

• Other types of user interfaces (3/3)
• Tangible user interfaces, which place a greater
  emphasis on touch and physical environment or its
  element.
• Text user interfaces are user interfaces which
  output text, but accept other form of input in
  addition to or in place of typed command strings.
• Voice user interfaces, which accept input and
  provide output by generating voice prompts. The
  user input is made by pressing keys or buttons,
  or responding verbally to the interface.
• Natural-Language interfaces - Used for search
  engines and on webpages. User types in a question
  and waits for a response.
• Zero-Input interfaces get inputs from a set of
  sensors instead of querying the user with input
  dialogs.
• Zooming user interfaces are graphical user
  interfaces in which information objects are
  represented at different levels of scale and
  detail, and where the user can change the scale
  of the viewed area in order to show more detail.

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!
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Man-Machine-Inferfaces
source http//www.sms.mavt.ethz.ch/flow_man_machi
ne
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Man-Machine-Inferfaces

• Natural Language User Interfaces are a type of
  computer human interface where linguistic
  phenomenon such as verbs, phrases, and clauses
  act as UI controls for creating, selecting, and
  modifying data in software applications.

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Man-Machine-Inferfaces

• http//www.abacuscorp.com/images/AI-28d.jpg

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Natural Language-Inferfaces

• Samples
• In the Man-Machine Interface Laboratory (MMIL)
  Abacus is investigating advanced man-machine
  interfaces and robotics.  Topics of special
  interest include integrated hardware and software
  systems involving (a) voice direction for users
  of computerized processes, (b) voice input and
  output, (c) natural language inputs and commands,
  (d) interactive graphics, (e) integrated expert
  systems, and (f) sensor control.  We are
  particularly concerned with experiments dealing
  with improvements to user-friendliness.
• The laboratory staff monitors technological
  developments in the above fields and develops
  prototype systems with the objective of
  transferring the technology to other Abacus
  projects such as the Natural Language Processing
  Project (NATLAN).

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Natural Language Processing

• 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

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the past - the present - the
future
What does Star Trek have to do with NLP ?
26
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
æ ç Þ ð t s sounds
Bill ... letters
Billy... words
Billy is eating his lunch. sentences
small (Billy) knowledge
Billy is mother consequences
child of
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applications of natural language systems
spoken text
written text
dialogue
understanding text
Speech input
Speech output
analysis
generation
Dialogue Systems
translation

• 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

• speaker voice recognition
• spoken commands /commandcontrol
• automatic dictation

• text-to-speech
• telephony
• IVR (interactive voice response)

• information systems
• DB query
• expert systems
• CALL
• robot stearing
• programming languages

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reading material
Latest edition Prentice Hall, 2008 ISBN-10
0131873210, ISBN-13 978-0131873216 First
chapter http//www.cs.colorado.edu/martin/SLP/Up
dates/1.pdf
29
reading material
http//cognet.mit.edu/library/books/view?isbn0262
133601
MIT Press, 1999, ISBN 0262133601 Reader link
http//www.amazon.de/gp/reader/0262133601/refsib_
dp_pt/028-2523061-0018166reader-page
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more...reading material (A.I./NLP)

• 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
31
more...reading material (A.I./NLP)

• 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. Whats 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
32
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
33
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.

34
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.
• -gt
• The vodka is strong but the meat is rotten.

35
history of NLP / CL

• How did it all start ?

• 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)

36
ELIZA pattern-matching (1/10)

• 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.

37
ELIZA pattern-matching (2/10) 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.

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ELIZA pattern-matching (3/10) How successful
is ELIZA ?

39
ELIZA pattern-matching (4/10) 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

40
ELIZA pattern-matching (5/10)How does ELIZA
work?

single keywords triggering a response
key xnone 0 answer Im not sure I understand
you fully- answer That is interesting. Please
continue. key sorry answer Please dont
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
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ELIZA pattern-matching (6/10) How does ELIZA
work?

keyphrases triggering a response with a
conversion
key I like xxx. (where xxx is an arbitrary
string) answer Why do you like xxx ? answer
Why do you say you like xxx ?
Example user I like xxx. ELIZA Why do you like
xxx?
42
ELIZA pattern-matching (7/10) How does ELIZA
work?

keyphrases triggering a response with a
conversion
key I am xxx. (where xxx is an arbitrary
string) answer Tell me why you think you are
xxx .
Example user I am very unhappy at the
moment. ELIZA Tell me why you think you are very
unhappy at the moment.
43
ELIZA pattern-matching (7/10) How does ELIZA
work?

keyphrases triggering a response with a
conversion plus postprocessing of reference words
key remember decomp I remember answer
Do you often think of (2) ? answer What else
do you recollect ?
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.
44
ELIZA pattern-matching (8/10)

Now its your turn ! (assignment 1) Try out
ELIZA, make up your own mind as to ELIZAs
realism. Get a first idea of man-machine
communication.
45
ELIZA pattern-matching (9/10)

to play with ELIZA (see following links) ELIZA
program  http//www.manifestation.com/neurotoys/e
liza.php3  http//www-ai.ijs.si/eliza-cgi-bin/eliz
a_script  http//www-ai.ijs.si/eliza/eliza.html  R
eading  http//i5.nyu.edu/mm64/x52.9265/january1
966.html  
46
ELIZA pattern-matching (10/10)

to play with ELIZA (see links below) ELIZA
program  http//www.manifestation.com/neurotoys/e
liza.php3  http//www-ai.ijs.si/eliza-cgi-bin/eliz
a_script  http//www-ai.ijs.si/eliza/eliza.html  R
eading  http//i5.nyu.edu/mm64/x52.9265/january1
966.html  
but now back to the history of NLP / CL
47
history of NLP / CL

• How did it all start ?

• 1970-1980 knowledge-based expert systems and
  natural language database interfaces,
  development of formal grammars (esp.
  syntax analysis)dialogue systems1972 SHRDLU
  (Winograd)1977 GUS (Bobrow et.al.), PAL (Sidner
  et.al.)natural language interfaces1972 LUNAR
  (Woods et.al.)1972-1976 RENDEVOUZ (Codd), REL
  (Thompson), REQUEST (Plath)1977 LIFER (Henrix),
  INTELLECT (Harris), PLANES (Waltz et.al.), CO-OP
  (Kaplan)

48
history of NLP / CL

• How did it all start ?

text understanding and text generating
systems1975 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)

49
history of NLP / CL

• How did it all start ?

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

• thats where we are today
• growing demand
• growing size of applications
• growing user expectations

50
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)

51
Natural language ... easy ?
Werner Heisenberg (theoretical physics) about
natural language and logics In logics we
regard linguistic constructions primarily under
the aspect of simple inference models....all
other linguistic structures are
neglected....natural language can describe
reality much better than we can do this using
logical inference procedures.
Does this mean natural language is easy and easy
to formalize ?
52
Natural language ... easy ?
Little Red Ridinghood Rotkäppchen
Do you remember the story of the little girl that
wore a red cape and which met a wolf while going
to her grandmothers house ?
Whats the problem ?
a little girl -gt in German, -chen is the
diminutive Don -gt Donny Kate -gt Katie , Bill -gt
Billy
53
Natural language ... easy ?
other application natural language database query
LanguageAccess (natural language interface to a
relational database) Sentence xy WHICH COUNTRY
EXPORTS FISH (/ PAUL) 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 (fishsubject) in
German with zero-article, its ambiguous
(disambiguation by case marking of the
article) SQL-query SELECT DISTINCT X1 COUNTRY,
X1.PRODUCT FROM EXPORTBASE X1 WHERE
X1.PCLASSFMF
54
Natural language ... easy ?
SENTENCE XY Who placed as many software orders
as Garzillo? SQL-query SELECT DISTINCT X.1 NAME,
X1.PURCHASENUMBER FROM PURCHASES X1, ORDERS X2
WHERE X1.PURCHASERNUMBERX2.PURCHASERNUMBER
GROUP BY X1.PURCHASERNUMBER, X1.NAME
HAVING COUNT () gt (SELECT COUNT ()
FROM ORDERS X3, PURCHASERS X4
WHERE X3.PURCHASERNUMBER X4.PURCHASERNUMBER
AND X4.NAMEGarzillo
GROUP BY X3.PURCHASERNUMBER)

55
Natural language ... easy ?

• 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.

56
Natural language ... easy ?
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
57
Natural language ... easy ?
language can do a lot....e.g. with
conjunctions NPNP I am eating a hamburger and a
pizza. VP-VP I will eat the hamburger and throw
away the pizza. S-S I eat a hamburger and Bill
eats a pizza. PP-PP I eat a pizza with ham and
with salami. ADJP-ADJP I eat a cold but
delicious hamburger. ADVP-ADVP I eat the
hamburger slowly and patiently. V-V I bake and
eat a hamburger. AUX-AUX I can and will eat a
hamburger. and even more.... ???-??? Mary is
sitting on and Bill under the table.
58
Natural language ... easy ?
language is analyzed on different levels
59
Natural language ... easy ?
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 ?
when you dont want to learn a database query
language to get data (Startrek) (textanalysis,
textgeneration, machine translation)
when you dont want to learn a programming
language to program your computer (machine
translation)
when you need to make a phonecall with someone in
Japan, but you dont speak Japanese (voice
recognition, machine translation)
when busy with your hands and you still want to
type (voice type)
when you want to evaluate millions of lines of
text (text/data mining)
when you are a slow typer (voice type)
when travelling (machine translation)
Back to the boom!
60
What do we need ?
dictionary
grammar
parser