Information pragmatics A Natural Language Processing Approach

1
Information pragmaticsA Natural Language
Processing Approach

• Christopher Manning
• CSLI IAP meeting
• November 2000
• http//nlp.stanford.edu/manning/

2
The problem

• When people see web pages, they understand their
  meaning
  
• By and large. To the extent that they dont,
  theres a gradual degradation
  
• When computers see web pages, they get only
  character strings and HTML tags

3
The human view
4
The intelligent agent view

• Ford Motor Company - Home Page
• trucks, SUV, mazda, volvo, lincoln, mercury,
  jaguar, aston martin, ford"
  
• Company corporate home page"
  
• WIDTH768
  
• HREF"default.asp?pageid473" onmouseover"logoOve
  r('fordscript')rolloverText('ht0')"
  onmouseout"logoOut('fordscript')rolloverText('ht
  0')"pt.gif" ALT"Learn more about Ford Motor Company"
  WIDTH"521" HEIGHT"39"
  

5
The problem (cont.)

• We'd like computers to see meanings as well, so
  that computer agents could more intelligently
  process the web
  
• These desires have led to XML, RDF, agent markup
  languages, and a host of other proposals and
  technologies which attempt to impose more syntax
  and semantics on the web in order to make life
  easier for agents.
• E.g., Guha (Epinions CTO/ex-Cyc, 1999) Very
  little of the information on the web is machine
  understandable. Need to move from a repository
  of data to a Web of Knowledge. RDF and the Open
  Directory might enable us to reach this goal.

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Ontologies

• The answer, it is suggested, is ontologies
• Shared formal conceptualizations of particular
  domains concepts, relations, objects,
  and constraints
  
• An ontology is a specification of a
  conceptualization that is designed for reuse
  across multiple applications
  
• Ontologies controlled vocabularies, taxonomy, OO
  database schema, knowledge-representation system
  
• Ontologies, as specifications of the concepts in
  a given field, and of the relationships among
  those concepts, provide insight into the nature
  of information produced by that field and are an
  essential ingredient for any attempts to arrived
  at a shared understanding of concepts in a field.

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Why is this idea appealing?

• An ontology is really a dictionary. A data
  dictionary.
  
• In the world of closed company databases, one had
  a clear semantics for fields and tables, and the
  ability to combine information across them by
  well-specified logical means
• In the world-wide web, you have a mess
• The desire for a global or industry-wide ontology
  is a desire to bring back the good old days.

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Thesis

• The problem cant and wont be solved by
  mandating a universal semantics for the web.

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Nuanced Thesis (1)

• Structured knowledge is important, and there will
  be increasing use of structure and keys just as
  we started using zipcodes, and then the
  postoffice started barcoding.
• These processes all offer the opportunity to
  increase speed and precision, and agents will
  want to use them when available and reliable
  
• But successful agents will need to be able to
  work even when this information isnt there.
  
• The postoffice still delivers your mail, even
  when the zipcode is missing or wrong.

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Nuanced Thesis/Theses? (2)

• There will never be a complete explicit and
  unambiguous semantics for everything needed on
  the web or even a non-trivial chunk of it
  both because of the scale of the problem and the
  speed of change
• Much of the semantic knowledge needs instead to
  reside in the agent
  
• The agent needs to be able to understand the
  human web, by reasoning using contextual
  information and its own knowledge, and various
  kinds of text and image processing

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XML?

• Im not saying that XML wont be used much. It
  certainly will be used widely
  
• e.g., News organizations moving to adopt NewsML
  for efficient production of electronic news
  Reuters, 11 October 2000
  
• Internally, it will be used for most content
  (except tabular data), so that content can be
  easily retargeted for browsers, WAP, iMode, and
  whatever comes next
• Some sites will publish XML to outside users.

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Will XML be published?

• Another lesson of transitions is that the old
  way persists for a very long time. The 4.0-level
  browsers will be with us for the
  foreseeable future.
• Dave Winer (reacting to similar
  conclusions of Jakob Nielsen)
  
• If youre going to be serving HTML for the
  foreseeable future, why bother complicating your
  life by serving something else as well?
  
• Especially when it doesnt look better to the
  user
  
• Or people might charge for XML, while giving HTML
  away for free

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XML

• Even when it is published, XML goes only a small
  way to enabling knowledge transfer
  
• It is simply a syntax
• The same meanings can be encoded by it in many
  ways, and conversely, different meanings can be
  coded in the same way.
  
• This is what suggests the need for a clearly
  mandated semantics for web markup

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Explicit, usable web semantics

• Will such a thing work?
• That is, will web pages be consistently marked up
  with a uniform explicit semantics that is easily
  processed by agents so that they dont have to
  deal with that messy HTML that underlies what
  humans look at?
• I think not. For a bunch of reasons.

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(1) The semantics

• Are there adequate and adequately understood
  methods for marking up pages with such a
  consistent semantics, in such a way that it would
  support simple reasoning by agents?
• No.

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What are some AI people saying?

• Anyone familiar with AI must realize that the
  study of knowledge representationat least as it
  applies to the commensense knowledge required
  for reading typical texts such as newspapersis
  not going anywhere fast. This subfield of AI has
  become notorious for the production of countless
  non-monotonic logics and almost as many logics of
  knowledge and belief, and none of the work shows
  any obvious application to actual
  knowledge-representation problems. Indeed, the
  only person who has had the courage to actually
  try to create large knowledge bases full of
  commonsense knowledge, Doug Lenat , is believed
  by everyone save himself to be failing in his
  attempt. (Charniak 1993xviixviii)

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(2) Many of the problems are pragmatics not
semantics

• pragmatic relating to matters of fact or
  practical affairs often to the exclusion of
  intellectual or artistic matters
  
• pragmatics linguistics concerned with the
  relationship of the meaning of sentences to their
  meaning in the environment in which they occur
  
• A lot of the meaning in web pages (as in any
  communication) derives from the context what is
  referred to in the philosophy of language
  tradition as pragmatics
• Communication is situated

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The crêperie

• After making use of 3 different picture search
  engines, and spending at least ½ an hour on the
  site of a very dedicated French photographer, I
  had found the setting for my story a crêperie.
• Well, almost. The visuals didnt really convey
  what I needed, so let me settle for a worse
  quality picture of a gyro shop.

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Not actually a crêperie
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Important points

• Multimedia information sources are vital
• The meaning of a text is strongly determined by
  its context of use
  
• Indeed, you can think of language as conveying
  the minimal amount of information necessary given
  the context and assumed shared knowledge
  
• Humans are used to communicating even when they
  dont completely hear or understand the signal
  even if this example is a bit extreme

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Pragmatics on the web

• Information supplied is incomplete humans will
  interpret it
  
• Numbers are often missing units
• A rubber band for sale at a stationery site is
  a very different item to a rubber band on a metal
  lathe
  
• A sidelight means something different to a
  glazier than to a regular person
  
• Humans will evaluate content using information
  about the site, and the style of writing
  
• value filtering

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(3) The world changes

• The way in which business is being done is
  changing at an astounding rate
  
• or at least thats what the ads from ebusiness
  companies scream at us
  
• Semantic needs and usages evolve (like languages)
  more rapidly than standards (cf. the Académie
  française)
  
• People use words that arent in the dictionary.
• Their listeners understand them.

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Rapid change

• Last year Rambus wasnt a concept in computer
  memory classification, now it is
  
• Cell phones have long had attributes like size
  and battery life
  
• Now whether they support WAP is an attribute
• In a couple of years time that attribute will
  probably have disappeared again
  
• People will introduce new products when theyre
  ready, not when some committee has added the
  terms to an ontology

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(4) Interoperation

• Ontology a shared formal conceptualization of a
  particular domain
  
• Meaning transfer frequently has to occur across
  the subcommunities that are currently designing
  ML languages, and then all the problems
  reappear, and the current proposals don't do much
  to help

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Many products cross industries

• http//www.interfilm-usa.com/Polyester.htm
• Interfilm offers a complete range of SKC's
  Skyrol brand polyester films for use in a wide
  variety of packaging and industrial processes.
  
• Gauges 48 - 1400
• Typical End Uses Packaging, Electrical, Labels,
  Graphic Arts, Coating and Laminating
  
• labels milk jugs, beer/wine, combination forms,
  laminated coupons,

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Mismatches

• When interoperation involves distinct domains or
  just distinct subcommunities within an industry,
  semantic mismatch ensues
  
• Local representational power conflicts with
  global consistency you want to advertise your
  new feature
  
• Your own needs will take priority
• Systems will need to deal with this
  heterogeneity
  
• Integration of information across XML markup
  languages is scarcely easier than integration of
  the same information represented in HTML.

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Semantic mismatches

• Different Usages
• Cell phone mobile phone
• Data projector beamer
• Different levels of specialized vocabulary
• water table the strip of wood that points
  outward at the bottom of the door
  
• hydrologists mean something very different by
  water table
  
• Ambiguity of reference
• Is C.D. Manning the same person as Christopher
  Manning?

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Name matching/Object identity knowledge

• Database theory is built around ideas of unique
  identifiers, determinate relational operations,
  
  
• (Human) natural language processing is built
  around context-embedded reasoning about issues of
  identity and meaning
  
• Around Stanford, the president is John Hennessy
• Elsewhere its well, either Gore or Bush
• Integrating information sources requires
  probabilistic reasoning about object identity

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(5) Pain but no gain

• A lot of the time people wont put in information
  according to standards for semantic/agent markup,
  even if they exist.
  
• Three reasons

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(5.1) Pain no gain

• Laziness
• Only 0.3 of sites currently use the (simple)
  Dublin Core metadata standard. (Lawrence and
  Giles 1999).
  
• Even less are likely to use something that is
  more work
  
• Why? They dont appear to perceive much value, I
  guess. What would change this?
  

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Inconsistency digital cameras

• Image Capture Device 1.68 million pixel 1/2-inch
  CCD sensor
  
• Image Capture Device Total Pixels Approx. 3.34
  million Effective Pixels Approx. 3.24 million
  
• Image sensor Total Pixels Approx. 2.11
  million-pixel
  
• Imaging sensor Total Pixels Approx. 2.11
  million 1,688 (H) x 1,248 (V)
  
• CCD Total Pixels Approx. 3,340,000 (2,140H x
  1,560 V )
  
• Effective Pixels Approx. 3,240,000 (2,088 H x
  1,550 V )
  
• Recording Pixels Approx. 3,145,000 (2,048 H x
  1,536 V )
  
• These all came off the same manufacturers
  website!!
  
• And this is a very technical domain. Try sofa
  beds.

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(5.2) Pain no gain

• Sell the sizzle, not the steak
• The way businesses make money is by selling
  something at a profit (for more than necessary)
  
• The way you do this is by getting people to want
  it from you
  
• advertising
• site stickiness (while Im here)
• trust
• Newspaper advertisements rarely contain spec
  sheets

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(5.2) Pain no gain

• Having an easily robot-crawlable site is a recipe
  for turning what you sell into a commodity
  
• This may open new markets
• But most would prefer not to be in this business
  
  
• Having all your goods turned into a commodity by
  a shopping bot isnt in your best interest.
  
• the profits are very low

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(5.3) Gain, no pain

• The web is a nasty free-wheeling place
• There are people out there that will abuse the
  intended use and semantics of any standard,
  providing they see opportunities to profit from
  doing so
• An agent cannot simply believe the semantics
• It will have to reason skeptically based on all
  contextual and world knowledge available to it.
  

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(6) Less structure to come

• the convergence of voice and data is creating
  the next key interface between people and their
  technology. By 2003, an estimated 450 billion
  worth of e-commerce transactions will be
  voice-commanded.
• Question will these customers speak XML tags?
• Intel ad, NYT, 28 Sep 2000
• Data Source Forrester Research.

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Summary so far

• With large-scale distributed information sources
  like the web, everyone suddenly needs to deal
  with highly heterogeneous data sources of
  uncertain correctness and value, where there are
  frequent semantic mismatches in which terms are
  used or what they mean. Contextual information
  is often needed to determine the meaning or
  reference of terms. In other words, the problems
  look a lot like Natural Language Processing,
  regardless of whether the data is text as
  narrowly defined.

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The connection to language

• Decker et al. IEEE Internet Computing (2000)
• The Web is the first widely exploited
  many-to-many data-interchange medium, and it
  poses new requirements for any exchange format
  
• Universal expressive power
• Syntactic interoperability
• Semantic interoperability
• But human languages have all these properties,
  and maintain superior expressivity and
  interoperability through their flexibility and
  context dependence

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The direction to go

• Successful agents will need prior knowledge, and
  use ontologies, etc. to help interpret web pages
  they become a locus of semantics
  
• But they will also depend on contextual knowledge
  and reasoning in the face of uncertain
  information.
  
• They will use well-marked up information, if
  available and trusted, but they will be able to
  extract their own metadata from information
  intended for humans, regardless of the form in
  which the information appears.

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The scale of the problem

• The web is too big a thing for it to be likely
  for humans to hand-enter metadata for most pages
  
• Hand-building ontologies and reasoning systems
  hasnt been very successful
  
• Agents must be able to extract propositions or
  relations from information intended for humans
  
• A useful observation in seeking this goal is that
  text statistics can often be used as a surrogate
  for world knowledge

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Processing textual data

• Use language technology to add value to data by
• interpretation
• transformation
• value filtering
• augmentation (providing metadata)
• Two motivations
• The large amount of information in textual form
• Information integration needs NLP-style methods

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Knowledge Extraction Vision

• Multi-dimensional Meta-data Extraction

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Task Text Categorization

• Take a document and assign it a label
  representing its content.
  
• Classic example decide if a newspaper article is
  about politics, business, or sports?
  
• But there are many relevant web uses for the same
  technology
  
• Is this page a laser printer product page?
• Does this company accept overseas orders?
• What kind of job does this job posting describe?
• What kind of position does this list of
  responsibilities describe?
  
• What position does this this list of skills best
  fit?

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Task Information Extraction / Wrapper Induction

• A lot of information that could be represented in
  a structured semantically clear format isnt
  
• It may be costly, not desired, or not in ones
  control (screen scraping) to change this.
  
• Information extraction systems
• Find and understand relevant parts of texts.
• Produce a structured representation of the
  relevant information relations (in DB sense)
  
• Goal being able to answer semantic queries using
  unstructured natural language sources

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Example Classified Ads

• 2067206v1
• March 02, 1998
• MADDINGTON 89,000
• OPEN 1.00 - 1.45
  
• U 11 / 10 BERTRAM ST
  
• NEW TO MARKET Beautiful
  
• 3 brm freestanding
  
• villa, close to shops bus
  
• Owner moved to Melbourne
  
• ideally suit 1st home buyer,
  
• investor 55 and over.
  
• Brian Hazelden 0418 958 996
  
• R WHITE LEEMING 9332 3477

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Real Estate Ads Output

• Output is database tables
• But the general idea in slot-filler format
• SUBURB MADDINGTON
• ADDRESS (11,10,BERTRAM,ST)
• INSPECTION (1.00,1.45,11/Nov/98)
• BEDROOMS 3
• TYPE HOUSE
• AGENT BRIAN HAZELDEN
• BUS PHONE [phone]
• MOB PHONE [phone]

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(No Transcript)
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Why doesnt text search (IR) work?

• What you search for in real estate
  advertisements
  
• Suburbs. You might think easy, but
• Real estate agents Coldwell Banker, Mosman
• Phrases Only 45 minutes from Parramatta
• Multiple property ads have different suburbs
• Money want a range not a textual match
• Multiple amounts was 155K, now 145K
• Variations offers in the high 700s but not
  rents for 270
  
• Bedrooms similar issues (br, bdr, beds, B/R )

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Task ParsingModern statistical parsers

• A greatly increased ability to do accurate,
  robust, broad coverage parsing
  
• Achieved by converting parsing into a
  classification task and using ML methods
  
• Statistical methods (fairly) accurately resolve
  structural and real world ambiguities
  
• Quickly rather than cubic complete parse
  algorithms, find best parse in linear time
  
• Provide probabilistic language models that can be
  integrated with speech recognition systems.

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From structure to meaning

• Syntactic structures aren't meanings, but heads
  and dependents essentially gives one relations
  
• orders(president, review(spectrum(wireless)))
• We don't do issues of noun phrase scope, but
  that's probably too hard for robust NLP
  
• Remaining problems synonymy and polysemy
• Words have multiple meanings
• Several words can mean the same thing
• But there are statistical methods for these
  tasks
  
• So the goal of transforming a text into relations
  of facts is close

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Precision Semantic markup

• The story so far
• We can get a fair way with text learning!
• In some places, moderate accuracy is okay
• But often business needs precision
• as Gio Wiederhold points out in his talks
• These methods may not offer sufficient accuracy

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Precision Semantic markup

• This is where semantic markup comes back in
• If a page has reliable semantic markup, such a
  program can use it to provide much higher
  accuracy levels
  
• Agents will need to check the provided markup
• But deciding that provided semantic markup is
  trustworthy is a lot easier (and hence more
  reliable) decision than working out the meaning
  from unstructured text

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Data verification

• Humans are very good at checking if data is
  reasonable
  
• 5525 Beverly Place, Pittsburgh
• 361-5525
• They know if content is reasonable by content
  analysis

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Data verification

• Most programs are dumb
• especially if they expect to just rely on
  semantic markup
  
• Again one needs unstructured text classification
  and learning
  
• one needs to check that field contents are
  reasonable
  
• Richly semantically marked up data has a real use
  here, since it allows agents to continue to learn
  (especially as usage changes over time)

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Conclusion

• Rich semantic markup has an important place
  improving the precision of agent understanding
  
• But there will be no substitute for agents that
  can work with unstructured data
  
• part of that data is text what I know about!
• but visual and other information is also
  incredibly important
  
• one really needs to use how a page looks
• All of it involves reasoning from uncertain
  situated information more in the style of NLP

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Thank you!