acl 2010
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ACL 2010. Thuy Dung Nguyen. Outlines. Our group work: keyprhase extraction system Invited talks Towards a Psycholinguistics of Social Interaction by Zenzi M Griffin, University of Texas at Austin Computational Advertising by Andrei Broder , Yahoo! Research IBM best student paper - PowerPoint PPT PresentationTRANSCRIPT
ACL 2010
Thuy Dung Nguyen
Outlines Our group work: keyprhase extraction system
Invited talks Towards a Psycholinguistics of Social Interaction by
Zenzi M Griffin, University of Texas at Austin Computational Advertising by Andrei Broder,
Yahoo! Research
IBM best student paper Extracting Social Networks from Literary Fiction by
David Elson, Nicholas Dames and Kathleen McKeown
SemEval Task 5: Keyphrase Extraction for Scientific Articles Our approach: utilize document logical structure (given
by ParsCit) identify which sections of the document contain the most
keyphrases shorten input text to contain only those sections: title, abstract,
introduction, related works, conclusion & 1st sentence of each paragraph of other sections
Þ increase precision but not sacrify recall.Þ final result : ranked 2nd out of 19 teams.
Other approaches: make use of document logical structure similar features: TFIDF, first occurrence, phrase length,
phrase’s occurrence in important sections, statistics of co-usage of keyphrases in large publication repository (HAL, Europarl)
Invited talk 1Study which factors influence errors in
addressing peopleby name
Shared roles Similar social relationship (boyfriend/girlfriend, family
members, dependants) Shared features
Gender, age, physical similarity Same initial sound in name (Cathy, Ken)
Invited talk 2 Computational Advertising Challenge: find “best match" between a given user
in a given context and a suitable advertisement.
Previous approach: matching based on similar words/phrases in both the webpage and the ad
Yahoo! Research: not only matches ads based on keywords but on the general topic. Classify webpages and ads into large tree of topics Map ad and webpage to a specific node on the tree Leverage the nodes for better matching
IBM best student paperExtracting Social Networks from Literary Fictions Construct social networks among characters
in 19th century British novels Provide evidence that these networks do not
fit 2 theories provided by literacy scholars There is an inverse correlation between the
amount of the dialogue and the number of characters
Novel setting (urban or rural) would have an effect on the structure of social network - more interactions occurring in rural communities than urban communities
IBM best student paper What’s the application of the research?
Using statistical method to test the validity of theories about social interaction in real world and their representation in novels
Others Best long paper
Beyond NomBank: A Study of Implicit Arguments for Nominal Predicates by Matthew Gerber and Joyce Chai
Challenge paper The Human Language Project: Building a
Universal Corpus of the World’s Languages, by Steven Abney and Steven Bird