September 5, 2001Melanie Martin - AI Seminar1 AI Seminar Our web page is at: gradrep Under...

September 5, 2001 Melanie Martin - AI Seminar 1

AI Seminar

Our web page is at:

www.cs.nmsu.edu/~gradrep

Under “Events” in left frame


Identifying Ideological Point of ViewPart II

Melanie Martin

September 5, 2001


Outline of this presentation Where are we??? Ideology Statistical NLP and Machine Learning Discourse features Internet Conclusion


Where are we???

Let’s 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…..


The Proposed System

IdeologicalClustering

TopicClustering,Filtering

Set of documents

on topic

Internet:Web pages,

Usenet

Docs ontopic

clustered by IPV

SearchEngine

User inputstopic


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


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


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


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


Ideology

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


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.


Ideology

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


Statistical NLP and ML

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



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



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



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)



We want to look a little more closely at Ando’s 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



Another option is a probabilistic classifier– assigns most probable class to an object

bases on a probability model– can we get around predefined classes?



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



We need to come up with a set of features…our next topic

Then deciding which features to use can be determined statistically with goodness of fit of graphical models



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


Discourse features

If we use probabilistic classifiers we need features, so we look at:

– linguistics– previous systems– discourse theory– literary theory


Discourse features

From linguistics and discourse: General strategy of most ideological

discourse (van Dijk’s Ideological Square):

– Emphasize positive things about Us– Emphasize negative things about Them– De-emphasize negative things about Us– De-emphasize positive things about Them


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)


Discourse features

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


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


Discourse features

Some discourse theories from Computational Linguistics

– Mann & Thompson (RST) (1988)– Grosz & Sidner (G&S) (1986)– Morris & Hirst (Lexical chains) (1991)


Discourse features

Issues

– implementation• G&S, RST

– finite number of fixed primitives• RST

– domain specific• RST depends on training


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


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


Discourse features

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


Discourse features

Lexical chains are easy to implement, but are unlikely to be sufficient…

For the next approximation: RST– Marcu’s implementation incorporating G&S– Mostly used for summarization and

generation– Would help get at the argument structure

of the text


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


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


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


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!!!


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


Conclusion It seems that such a system can be built

– find a good search engine– use Kleinberg’s 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


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

September 5, 2001Melanie Martin - AI Seminar1 AI Seminar Our web page is at: gradrep Under...

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