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  1. Transactions on the Web (TWEB)
  2. ACM Transactions on the Web (TWEB) : Volume 6
  3. Issue 3, September 2012
  4. Extracting information networks from the blogosphere
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ACM Transactions on the Web (TWEB) : Volume 10
ACM Transactions on the Web (TWEB) : Volume 9
ACM Transactions on the Web (TWEB) : Volume 8
ACM Transactions on the Web (TWEB) : Volume 7
ACM Transactions on the Web (TWEB) : Volume 6
Issue 4, November 2012
Issue 3, September 2012
A model-driven methodology to the content layout problem in web applications
Extracting information networks from the blogosphere
FoXtrot: Distributed structural and value XML filtering
Navigating tomorrow's web: From searching and browsing to visual exploration
Issue 2, May 2012
Issue 1, March 2012
ACM Transactions on the Web (TWEB) : Volume 5
ACM Transactions on the Web (TWEB) : Volume 4
ACM Transactions on the Web (TWEB) : Volume 3
ACM Transactions on the Web (TWEB) : Volume 2
ACM Transactions on the Web (TWEB) : Volume 1

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Extracting information networks from the blogosphere (2012)

Extracting information networks from the blogosphere

Content Provider ACM Digital Library
Author Merhav, Yuval Mesquita, Filipe Frieder, Ophir Barbosa, Denilson Yee, Wai Gen
Copyright Year 2012
Abstract We study the problem of automatically extracting information networks formed by recognizable entities as well as relations among them from social media sites. Our approach consists of using state-of-the-art natural language processing tools to identify entities and extract sentences that relate such entities, followed by using text-clustering algorithms to identify the relations within the information network. We propose a new term-weighting scheme that significantly improves on the state-of-the-art in the task of relation extraction, both when used in conjunction with the standard $\textit{tf}$ ċ $\textit{idf}$ scheme and also when used as a pruning filter. We describe an effective method for identifying benchmarks for open information extraction that relies on a curated online database that is comparable to the hand-crafted evaluation datasets in the literature. From this benchmark, we derive a much larger dataset which mimics realistic conditions for the task of open information extraction. We report on extensive experiments on both datasets, which not only shed light on the accuracy levels achieved by state-of-the-art open information extraction tools, but also on how to tune such tools for better results.
Starting Page 1
Ending Page 33
Page Count 33
File Format PDF
ISSN 15591131
e-ISSN 1559114X
DOI 10.1145/2344416.2344418
Volume Number 6
Issue Number 3
Journal ACM Transactions on the Web (TWEB)
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-10-02
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Clustering Domain frequency Named entities Open information extraction Relation extraction
Content Type Text
Resource Type Article
Subject Computer Networks and Communications
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