The Problem of Context in Sentence Production Surely A Case to ReConvene the Data Base Task Group De

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The Problem of Context in Sentence
ProductionSurely A Case to Re-Convene the Data
Base Task Group?Derek J. SMITHCentre for
PsychologyUniversity of Wales Institute,
Cardiffdsmith_at_uwic.ac.ukhttp//www.smithsrisca.d
emon.co.uk
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As presented to the3rd International
Conferenceon Computing, Communications, and
Control TechnologiesAustin, TX, Wednesday 27th
July 2005

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A BIT MORE ABOUT THE AUTHOR

• 1980s - specialized in the design and operation
  of very large DBTG databases.
• Since 1991 taught cognitive science and
  neuropsychology to Speech and Language
  Pathologists.
• Hence interdisciplinary in database, cognitive
  neuropsychology, and psycholinguistics.

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THE PROBLEM

• Information systems love duplicating your data.
  E.g. duplicate postings to a transaction file,
  duplicate entries in a master file, entire
  duplicated files (try moving house, and see how
  long it takes for the old address to stop being
  used!)
• To help cure this problem, by the late 1950s
  steps had been taken to specify organizational
  data more accurately using data dictionaries
  and data models".
• This accumulation of "metadata" - data about data
  was then used to specify a central
  shared-access data base, and the software
  products which managed the whole process became
  known as "database management systems", or
  "DBMSs".
• And yet those not directly involved with DBMSs
  know little about their technical construction,
  evolution, or how to design or operate them
  effectively (Haigh, 2004).

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THE PLAN OF ATTACK

• This paper concerns itself with the network
  database.
• It reminds us of a little of the history, not
  just of the database itself, but of the whole
  idea of associative networks ..
• .. and then considers the trans-disciplinary
  relevance of the underlying concepts and
  mechanisms to the science of psycholinguistics,
  because they might just help solve some of that
  science's long-standing problems.

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NETWORK DATABASE HISTORY (1)

• The story of the network database begins in the
  early 1960s at the General Electric Corporation's
  laboratories in New York, where Charles W.
  Bachman had been given the job of building GE a
  DBMS.
• The resulting system was the "Integrated Data
  Store" (IDS), and was built around a clever
  combination of two highly innovative design
  features, namely (1) a direct access facility
  similar to IBMs acclaimed RAMAC, and (2)
  Bachmans own "data structure diagram" (soon to
  become famous as the "Bachman Diagram").
• This is how the direct access part of the
  equation is implemented ..

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NETWORK DATABASE HISTORY (2)

• Bachman Diagrams prepare your data for maximum
  usability by analyzing it on a set owner/set
  member basis.
• Owner records are stored using the direct access
  facility, and their related members are
  identified using chain pointer addressing.
• "Via-clustering" is often used to keep member
  records physically close to their owners. (This
  cannot always be done, but is very efficient in
  disc accesses when it can be.)

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NETWORK DATABASE HISTORY (3)

• And here, from Maurer and Scherbakov (2005,
  online) is a typical owner-member set (left) and
  the corresponding Bachman Diagram (right) ..

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NETWORK DATABASE HISTORY (4)

• Bachman had the IDS prototype running early 1963,
  and by 1964 it was managing GE's own stock
  levels.
• Initial user feedback was so positive that the
  Bachman-GE approach soon came to the attention of
  CODASYL, the committee set up by the Pentagon in
  May 1959 to produce a general purpose programming
  language.
• However, CODASYL had published the specifications
  for COBOL in January 1960, so it predated IDS by
  four years and had accordingly not been designed
  to support the particular processing requirements
  of DBMSs ..

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NETWORK DATABASE HISTORY (5)

• In fact, it was so difficult for COBOL to
  implement IDS's chain pointer sets (or "lists")
  of records, that in October 1965 CODASYL
  established a List Processing Task Force (LPTF)
  to look into possible improvements to the
  specification.
• The LPTF meetings immediately became so dominated
  by database issues in general that they renamed
  themselves the Data Base Task Group (DBTG).
• We may thus refer to IDS as a DBTG database, a
  CODASYL database, or a network database. All
  these terms are synonymous and used
  inter-changeably in the literature.

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NETWORK DATABASE HISTORY (6)

• A curious turn of events then saw IDS development
  taken over by one of GE's early customers, the
  B.F. Goodrich Chemical Corporation. They had been
  highly impressed with IDS, but wanted greater
  functionality, so they bought the rights to
  develop an IBM version.
• By 1969, Goodrich were able to market their
  improved system in its own right, badging it as
  the "Integrated Database Management System"
  (IDMS). The new product was heavily deployed in
  the 1980s, and survives to this day as Computer
  Associates' CA-IDMS, in which incarnation it
  continues to power many of the world's heaviest
  duty on line systems.
• Bachman was awarded the 1973 A.C.M. Turing Award
  for his achievements ..

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NETWORK DATABASE HISTORY (7)

• .. however, its just possible that Bachman had
  actually been cleverer with his network memory
  technology than even he or the Turing Awards
  panel realized ..
• .. because in historical terms the idea of
  associative memory underlies much of psychology
  as well. Indeed, it dates all the way back to the
  classical Greek philosophers.
• So now for some history from a totally different
  discipline ..

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THE BIRTH OF COGNITIVE SCIENCE (1)

• Aristotle had suggested back in around 350 BCE
  that memory was based on the "incidental
  association" of one stored concept with another.
• That same general orientation went on to give its
  name to the entire "Associationist" tradition of
  philosophy, culminating in Freud's "association
  of ideas" technique of psychoanalysis and the
  modern connectionist net and semantic network
  industries.
• Data networks, in other words, are nothing new to
  students of the mind, and in this paper we are
  going to select one application in particular for
  attention ..

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THE BIRTH OF COGNITIVE SCIENCE (2)

• .. namely early attempts at machine translation
  (MT).
• It was one of these early "computational
  linguists" - Cambridge University's Richard H.
  Richens, plant geneticist by profession but
  self-taught database designer into the bargain -
  who first coined the popular modern term
  "semantic net" (Richens, 1956, p23).
• Richens was actually half of an important
  research partnership in the history of cognitive
  science. Just after the war he had toyed with
  Hollerith technology to help him analyze his
  genetics research data, and had ended up with a
  rudimentary punched-card database.
• This experience convinced him that with the right
  arrangement of data and greater processing power
  it ought to be possible to automate most
  anything, including natural language translation.
  So he began designing the card layouts for a
  bilingual machine dictionary making him
  arguably the first database designer?

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THE BIRTH OF COGNITIVE SCIENCE (3)

• Richens then discovered that his enthusiasm for
  MT was shared by a University of London
  crystallographer named Andrew D. Booth, himself
  something of an expert in computers. During WW2,
  Booth had been a boffin in the rubber industry,
  x-raying slices of rubber from destroyed enemy
  aircraft and vehicles. And because X-ray
  crystallography generates a lot of numbers, Booth
  had built calculating machinery to assist him. He
  had continued this work when he got a peacetime
  lectureship at Birkbeck College.
• This research had then brought him to the
  attention of the US National Defense Research
  Committee's Warren Weaver. Weaver duly met with
  Booth on 8th March 1947 while the latter was on a
  fact-finding visit to the University of
  Pennsylvania's Moore School of Engineering.
• Weaver was so enthusiastic about what Booth had
  to say that he used his influence to put him
  forward for a study scholarship under John von
  Neumann at Princeton's Institute of Advanced
  Studies.

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THE BIRTH OF COGNITIVE SCIENCE (4)

• Booth was at Princeton from March to September
  1947, and upon his return to Britain proceeded to
  build a small relay i.e. electromechanical
  computer, complete with one of the first magnetic
  drum memories (10 years before IBMs RAMAC).
• A chance meeting of minds then changed the world.
  It took place between Richens and Booth on 11th
  November 1947 (Hutchins, 1997), and focused on
  the pair's shared interest in MT.
• They concluded that Booth's magnetic drum might
  provide the sort of random access technology
  needed to host Richens' proposed lexical database
  - giving them 15 years prior claim to Bachman's
  basic IDS architecture.

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THE BIRTH OF COGNITIVE SCIENCE (5)

• There followed a decade of collaborative research
  during which this - and eventually many other
  teams found out just how complicated natural
  language really was!
• To start with, MT took the scientific world by
  storm, with the first MT conference being
  organized at MIT by Yehoshua Bar-Hillel (an MT
  skeptic). This took place 17-20th June 1952.
• Centers of academic excellence soon emerged at
  MIT (Victor Yngve), Washington (Erwin Reifler),
  and Berkeley (Sydney Lamb). Britain's effort was
  concentrated at the Cambridge Language Research
  Unit, under Margaret Masterman, where the
  researchers included Richens himself, Frederick
  Parker-Rhodes, Yorick Wilks, Michael Halliday,
  and Karen Spärck Jones.

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THE BIRTH OF COGNITIVE SCIENCE (6)

• In short, this was interdisciplinary science at
  its best, and its target language - lay at the
  very heart of cognition. We therefore date the
  birth of cognitive science to that foggy November
  1947 meeting between Richens and Booth.
• Semantic networks are now a major research area
  within AI (for an excellent review, see Lehmann,
  1992).

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OUR PROBLEM AND OUR PLAN

• However, as an IDMS designer-programmer turned
  cognitive scientist, our personal complaint is
  that network researchers typically ignore the
  explanatory and practical potential of the
  network database.
• To help restore the balance, the present paper
  will explore how IDMS concepts might help with
  one of cognitive sciences most troublesome
  problems, that of context in speech production.
• So let us move away from all the history and look
  at some modern psycholinguistics, because if
  computing is the father of machine translation,
  psycholinguistics is its mother.
• Specifically, we need to look at the staged
  cognitive processing which takes place during
  speech production.
• WARNING Language and speech are - crucially
  - NOT THE SAME THING, as we shall shortly be
  seeing.

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SPEECH PRODUCTION STAGES (1)

• The notion that voluntary speech production
  involves a succession of hierarchically organized
  processing stages may be seen in a number of
  influential 19th century models of cognition, but
  the subject was largely ignored until UCLA's
  Victoria A. Fromkin reawakened interest in it in
  the early 1970s (Fromkin, 1971).
• Fromkin proposed six processing stages. The first
  three stages constitute the language part of the
  speech and language equation, while the latter
  three provide the speech to go with it.
• Reassuringly, there is virtual unanimity amongst
  authors ancient and modern as to where in the
  overall scheme of things to place the bulk of the
  semantic network ..
• .. you simply attach the semantic network to the
  command and control module at the top of the
  cognitive hierarchy, to serve as that module's
  resident knowledge base.

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SPEECH PRODUCTION STAGES (2)

• The result is a mental champagne-cascade ..
• .. with ideas pouring down from the top ..
• .. words being added on the way down ..
• ..... giving you your language .....
• .. sounds being added below that .....
• ..... and linear speech emerging at the bottom.

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SPEECH PRODUCTION STAGES (3)

• This diagram is from Ellis (1982) and shows how
  psycholinguists typically summarize the flow of
  information between cognitive modules.
• Click here to see full sized diagram and here for
  a detailed explanatory commentary.

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SPEECH PRODUCTION STAGES (4)

• Here we see the speech production (lower left)
  leg of Ellis (1982) in close-up.
• Note the three successive modules. Fromkins six
  stages map roughly two each onto these
  hierarchically separated processing levels ..

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SPEECH PRODUCTION STAGES (5)STAGE 1 - PURE
IDEATION

• Stage 1 - Propositional Thought This is the
  selective activation of propositions within the
  semantic network, as part of the broader
  phenomenon of reasoning, and it is vitally
  important to students of the mind because it
  establishes the semantic context for whatever
  happens next, and especially the use and
  interpretation of words.
• This stage is known by Associationist
  epistemologists as "ratiocinative" thought.

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SPEECH PRODUCTION STAGES (6)STAGE 2 - SPEECH
ACTS

• Stage 2 - Speech Act Volition This is where a
  carefully selected subset of the aforementioned
  stream of propositions is converted into a
  "speech act" of some sort.
• Speech acts are preverbal linguistic
  manipulations of the social environment, each
  calculated to achieve some discrete behavioral
  effect.
• Fully functioning adult humans have a repertoire
  of around 1000 different speech acts to choose
  from (see Bach and Harnish, 1979, for a fuller
  list).
• In the Chomskyan sense, speech acts give us much
  of our "deep" sentence structure.
• This structure is what gets passed down to Stage
  3, thus interfacing the original thought with
  the spoken word.

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SPEECH PRODUCTION STAGES (7)THE POINT ABOUT
SPEECH ACTS

• Because it is the final outcome which matters,
  speech acts are free to generate sentences which
  use words ironically or figuratively.
• E.g. such everyday phrases as
• "when you have a moment" (i.e. now)
• and
• "if you don't mind" (i.e. whether you do or not).

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SPEECH PRODUCTION STAGES (8)THE POINT ABOUT
ENCODING

• Note very carefully that all the mental content
  we have talked about so far has been NONVERBAL.
• In fact, you should think of it as encoded in
  images, icons, sprites, ideograms, etc.,
  both concrete and abstract.
• This is very awkward in practice, because you
  usually end up having to describe in words
  something whose very essence is that the words
  haven't yet been selected.

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SPEECH PRODUCTION STAGES (9)STAGE 3
LEXICALIZATION(REPLACING IDEAS WITH WORDS)

• Stage 3 Word Finding The deep structure
  produced by Stage 2 is now passed block by block
  (grammarians call them "phrases") down the motor
  hierarchy.
• Stage 3 determines the surface words to be used
  and how they will need to be combined
  syntactically. Identifying the agent of a
  sentence is particularly vital. For example,
  consider the ideation ltIDEO Fidogt ltIDEO
  bitegt ltIDEO Derekgt ltSPEECH ACT warngt.
• If you get the agent-object relationship
  confused, then the sentence Derek bites Fido
  will be just as likely to occur as Fido bites
  Derek.

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CONTEXT IN SPEECH PRODUCTION (1)THE PROBLEM OF
PRONOUNS

• There is an even bigger problem with pronouns,
  thus .....
• Fido is going to bite Derek
• Fido is going to bite him
• He is going to bite Derek
• He is going to bite him
• Context allows the most appropriate NOUN-PRONOUN
  option to be selected, hence the process is
  highly sensitive to the prior state of the
  concept network, IN BOTH SPEAKER AND LISTENER.
• Indeed, it is fair to say that it is the minds
  context maintenance mechanisms whatever they
  are which allow everyday conversation to rely
  so heavily on what is NOT being said!

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CONTEXT IN SPEECH PRODUCTION (2)THE PROBLEM OF
DEIXIS

• The use of language to point in some way at a
  thing referred to is known as "deixis". Here are
  some examples of its subtypes .....
• Example 1 "It is bad enough when it might have
  been mentioned many words beforehand, but you
  also get forward deixis, where the referent is
  still to come.
• Example 2 They have particular problems with
  pronoun deixis, MT programmers, because they have
  to work out - occasionally from phrases not yet
  spoken - what they are supposed to be
  translating.
• Example 3 "You also get non-explicit deixis,
  where the referent is left to establish itself
  without specific mention, as in 'They are out to
  get me' .

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A CROSS-DISCIPLINARY EXPERIMENT

• So what would happen if we used IDMS - a network
  architecture by design - to implement the
  knowledge network at the top of the speech motor
  hierarchy?
• Would its systems internals be able to cope
  (where rival systems have not) with the combined
  load of philosophical, psychological,
  psycholinguistic, and linguistic problems?
• Specifically, might it help machines master
  language as well as speech?
• Well it is going to take a sustained research
  effort to answer these questions fully, but the
  DBTG metaphor certainly promises much in three
  important areas, as follows .....

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DBTG PROMISE 1HASH RANDOM ADDRESSING

• The IDMS hash random facility would be ideal for
  storing noun concepts such as ltIDEO Fidogt and
  ltIDEO Derekgt ..
• .. giving us cumulatively our personal knowledge
  base.

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DBTG PROMISE 2CHAIN POINTER ADDRESSING

• The IDMS chain pointer facility is already ideal
  for implementing Bachmans logical data sets,
  weaving the individual data fragments into a
  complex yet "navigable" lattice.
• Chain pointers thus give more than two
  millennia's worth of philosophers their
  associative network.

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DBTG PROMISE 3SET CURRENCY ADDRESSING

• Perhaps more importantly still, the IDMS device
  known as the "set currency" does for the DBTG
  database what calcium-modulated synaptic
  sensitization appears to be doing in biological
  memory systems (Smith, 1997).
• Biological set currencies allow specific memories
  to be sustained up to an hour after first
  activation. E.g the pronoun him in the earlier
  example can point to one noun in particular out
  of potentially many tens of thousands.

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DBTG PROMISE 3A BIOLOGICAL SET CURRENCY?

• Readers may simulate the phenomenon of memory
  sensitization right now by trying to recall the
  year of Bar-Hillel's MIT conference. You have ten
  seconds ..

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DBTG PROMISE 3A BIOLOGICAL SET CURRENCY?

• The year in question 1952 - is perhaps ten
  minutes of listening time ago, but its engram
  its memory trace - is nonetheless still in a
  raised state of excitation ..

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DBTG PROMISE 3A BIOLOGICAL SET CURRENCY?

• .. It is long-term memory left "glowing" in some
  way by the original activation.
• This is possibly the mechanism of maintaining
  referential context and supporting deixis over
  time-extensive thought or conversation.
• Click here for a more detailed introduction to
  the biochemistry of memory.

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CONCLUSION (1)

• We have been considering the trans-disciplinary
  relevance of the concepts and mechanisms
  underlying the DBTG database to the science of
  psycholinguistics.
• Our central complaint was that despite a long
  tradition of semantic network simulations in
  computational linguistics none of the established
  research technologies really implements a network
  data model as a network physical form. Instead,
  they prefer to keep the physical storage
  relatively simple, typically in a "flat file"
  format.
• By contrast, the only architecture which has ever
  been able to cope with volatile data in bulk is
  the DBTG architecture. This is because it is
  largely self-indexing, often via- clustered, and
  uses pre-allocated expansion space. (This is
  precisely why CA-IDMS is still supporting the
  heavy end of the world's OLTP industry, despite
  repeated attempts to dislodge it.)

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CONCLUSION (2)

• Our humble (and not entirely tongue-in-cheek)
  proposal is therefore that the DBTG - having
  delivered on behalf of the volatile data industry
  in the 1960s - now needs to be reconstituted in
  the interests of a better understanding of the
  mind - the ultimate database.
• We are ourselves currently researching the nature
  of the interdisciplinary collaboration which such
  an exercise would involve.

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REFERENCES

• Bach, K. and Harnish, R.M. (1979). Linguistic
  Communication and Speech Acts. Cambridge, MA
  MIT Press.
• Fromkin, V.A. (1971). The non-anomalous nature of
  anomalous utterances. Language, Vol. 47, pp.
  27-52.
• Haigh, T. (2004). A veritable bucket of facts. In
  M. E. Bowden and B. Rayward (Eds.), The History
  and Heritage of Scientific and Technical
  Information System, Medford, NJ Information
  Today.
• Hutchins, W.J. (1997). From first conception to
  first demonstration. Machine Translation, Vol.
  12, No. 3, pp. 195-252.
• Lehmann, F. (Ed.) (1992). Semantic Networks in
  Artificial Intelligence. Oxford Pergamon. Being
  a special issue of the journal Computers and
  Mathematics with Applications, 23(2-9).
• Maurer, H. and Scherbakov, N. (2005, online).
  Network (CODASYL) Data Model. Electronic
  document retrieved 17th July 2005 from
  http//coronet.iicm.edu/wbtmaster/allcoursesconten
  t/netlib/ndm1.htm)
• Richens, R.H. (1956). Preprogramming for
  mechanical translation. Mechanical Translation,
  Vol. 3, No. 1, pp. 20-25.
• Smith, D.J. (1997). The IDMS Set Currency and
  Biological Memory. Cardiff UWIC. ISBN
  1900666057 Workbook to support poster presented
  10th March 1997 at the Interdisciplinary Workshop
  on Robotics, Biology, and Psychology, Department
  of Artificial Intelligence, University of
  Edinburgh.