AI Seminar

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AI Seminar

• Our web page is at
• www.cs.nmsu.edu/gradrep
• Under Events in left frame

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Identifying Ideological Point of ViewPart II

• Melanie Martin
• September 5, 2001

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Outline of this presentation

• Where are we???
• Ideology
• Statistical NLP and Machine Learning
• Discourse features
• Internet
• Conclusion

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Where are we???

• Lets recall what we want to do
• Build a system that could take information from
  web pages and Usenet newsgroups on a given topic
  and segment, classify or cluster it by
  ideological point of view..

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The Proposed System
User inputs topic
Ideological Clustering
Topic Clustering, Filtering
Set of documents on topic
Search Engine
Internet Web pages, Usenet
Docs on topic clustered by IPV
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Where are we???

• What do we need?
• A computationally feasible definition of
  ideological point of view
• A search engine, possibly with additional
  processing, to produce a collection of documents
  on the topic specified by the user

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Where are we???

• What else do we need?
• A module to cluster documents by ideological
  point of view
• A user interface
• A way to evaluate the system

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Where are we???

• Why do we need this?
• Some examples using google
• query back pain 2,220,000
• scoliosis 121,000
• query lyme disease 163,000
• query zoning shopping center 65,100
• (add) clark county nv 299
• query un racism conference 74,000

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Outline of this presentation

• Where are we???
• Ideology
• Statistical NLP and Machine Learning
• Discourse features
• Internet
• Conclusion

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Ideology

• Working definition from van Dijk Ideologies are
  the fundamental beliefs of a group and its
  members.
• instantiated as Us vs. Them
• predefined ideologies will not work across
  domains
• want to avoid researcher bias
• definition likely needs more work

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Ideology

• Linguistics
• van Dijk (1998)
• Blommaert Verschueren (1998)
• Wang (1993)
• Wortham Locher (1996)

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Ideology

• The Systems
• Ideology Machine -1965 to 1973 - Abelson et al.
• Politics - 1979 - Carbonell
• Pauline - 1987 - Hovy
• Tracking Point of View in Narrative - 1994 -
  Wiebe
• Spin Doctor - 1994 - Sack
• Terminal Time - 2000 - Mateas et al.

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Ideology

• Some issues
• Evaluation!!!
• Hard-coded knowledge
• Domain dependence
• Cognitive plausibility
• More precise definitions

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Outline of this presentation

• Where are we???
• Ideology
• Statistical NLP and Machine Learning
• Discourse features
• Internet
• Conclusion

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Statistical NLP and ML

• Two techniques we will consider
• Latent Semantic Analysis
• Probabilistic Classification

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Statistical NLP and ML

• Issues
• clustering versus classification
• categories may not be predefined
• may want to take a variety of features into
  account
• favor learning over hard-coding knowledge
• supervised versus unsupervised
• cost of annotated training data

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Statistical NLP and ML

• Latent Semantic Analysis
• text represented as a matrix
• entries are weighted frequency of word in context
• semantic space obtained through SVD
• words appearing in similar context have similar
  feature vectors
• characterizes semantic content of words in context

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Statistical NLP and ML

• Why LSA is a good choice here
• semantics is key component of ideological
  discourse
• clustering without need for predefined categories
• already shown useful for
• summarization (Ando 2000)
• text segmentation (Choi 2001)
• measuring text coherence (Foltz 1998)

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Statistical NLP and ML

• We want to look a little more closely at Andos
  work
• uses term, sentence, and document vectors
• modified SVD algorithm
• interesting interface
• Multi-document summarization by visualizing
  topical content. Rie Kubota Ando,
  Branimir Boguraev, Roy Byrd, and Mary Neff.
  ANLP/NAACL '00 Workshop on Automatic Summarization

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Statistical NLP and ML

• Another option is a probabilistic classifier
• assigns most probable class to an object bases on
  a probability model
• can we get around predefined classes?

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Statistical NLP and ML

• Probability model
• defines joint distribution of variables
• set of feature variables and a class variable
• Wiebe and Bruce (1995) got around the issue of
  not knowing the classes in advance by breaking up
  the problem and using a series of classifiers

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Statistical NLP and ML

• We need to come up with a set of featuresour
  next topic
• Then deciding which features to use can be
  determined statistically with goodness of fit of
  graphical models

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Statistical NLP and ML

• Both methods seem to have a lot of potential
• LSA would be easier to implement
• possibly a baseline for evaluation of
  probabilistic classifiers
• Less linguistic knowledge gain likely with LSA

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Outline of this presentation

• Where are we???
• Ideology
• Statistical NLP and Machine Learning
• Discourse features
• Internet
• Conclusion

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Discourse features

• If we use probabilistic classifiers we need
  features, so we look at
• linguistics
• previous systems
• discourse theory
• literary theory

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Discourse features

• From linguistics and discourse
• General strategy of most ideological discourse
  (van Dijks Ideological Square)
• Emphasize positive things about Us
• Emphasize negative things about Them
• De-emphasize negative things about Us
• De-emphasize positive things about Them

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Discourse features

• How are these strategies instantiated in
  discourse? (van Dijk)
• What is there
• argument structure
• syntactic patterns
• style and non-literal language
• actor descriptions
• thematic structure
• topoi (standardized topics)

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Discourse features

• What is not there
• implication
• presupposition
• inference
• goals and plans

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Discourse features

• Disclaimers, selected examples
• Apparent Negation I have nothing against X,
  but...
• Apparent Concession They may be very smart,
  but...
• Apparent Empathy They may have had problems,
  but...
• Apparent Effort We do everything we can, but...
• Positive self-representation and face keeping

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Discourse features

• Some discourse theories from Computational
  Linguistics
• Mann Thompson (RST) (1988)
• Grosz Sidner (GS) (1986)
• Morris Hirst (Lexical chains) (1991)

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Discourse features

• Issues
• implementation
• GS, RST
• finite number of fixed primitives
• RST
• domain specific
• RST depends on training

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Discourse features

• A reasonable first approach Lexical Chains
  (Morris Hirst)
• Sequences of related words spanning a topical
  unit in the text
• based on lexical cohesion
• encapsulates context
• helps identify key phrases

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Discourse features

• Idea of Algorithm
• read next word
• if candidate
• check chains within suitable span
• check thesaurus or WordNet
• check other knowledge sources
• if found
• include in chain
• recalculate chain

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Discourse features

• Lexical chains could help us in
• topic segmentation
• intentional structure
• lexical features for a classifier

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Discourse features

• Lexical chains are easy to implement, but are
  unlikely to be sufficient
• For the next approximation RST
• Marcus implementation incorporating GS
• Mostly used for summarization and generation
• Would help get at the argument structure of the
  text

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Discourse features

• RST Basics
• about 23 rhetorical relations
• account for discourse coherence
• link adjacent spans of text
• 5 schema
• defined in terms of relations
• specify how spans can co-occur
• nucleus and satellite spans
• end up with tree structure

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Discourse features

• Would most likely use RST to generate features
  for a classifier or as input to a pattern
  recognizer
• Nuclei spans help pick out the more important
  segments of text
• Produces a tree that gives the structure of the
  rhetorical structure of the text

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Outline of this presentation

• Where are we???
• Ideology
• Statistical NLP and Machine Learning
• Discourse features
• Internet
• Conclusion

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Internet

• We would like to mine the structure of the
  internet
• see if there is a correspondence with groups
• improved IR by topic
• figure out what search engine to use as a base
  for our system

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Internet

• Issues
• topic or query disambiguation
• what is a minimal unit
• how to use the structure of the web
• finding authorities
• communities and subgraphs
• Evaluation!!!

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Internet

• Kleinberg (1997)
• link based model
• hub - links to many related authorities
• authority
• iterative weighting algorithm that converges
  (rapidly in practice)
• can disambiguate authorities by sense
• can be used to trawl for cyber communities

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Outline of this presentation

• Where are we???
• Ideology
• Statistical NLP and Machine Learning
• Discourse features
• Internet
• Conclusion

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Conclusion

• It seems that such a system can be built
• find a good search engine
• use Kleinbergs algorithm to improve collection
  of documents retrieved
• use LSA and/or a probabilistic classifier to
  handle the ideological point of view
• with a probabilistic classifier use linguistic
  and discourse features
• develop evaluation methodolgy

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The End

• Thanks for listening!
• If you want to know more, my Comprehensive Exam
  paper is at
• www.CS.NMSU.Edu/mmartin/courses/comps_all.html