NDLI: Paraphrase acquisition via crowdsourcing and machine learning

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Issue 4(Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers), September 2013

Issue 3(Special Sections on Paraphrasing; Intelligent Systems for Socially Aware Computing; Social Computing, Behavioral-Cultural Modeling, and Prediction), June 2013

Introduction to special section on paraphrasing

Using targeted paraphrasing and monolingual crowdsourcing to improve translation

Distributional phrasal paraphrase generation for statistical machine translation

Generating targeted paraphrases for improved translation

An abstractive approach to sentence compression

An inference-based model of word meaning in context as a paraphrase distribution

Paraphrase acquisition via crowdsourcing and machine learning

Multitechnique paraphrase alignment: A contribution to pinpointing sub-sentential paraphrases

Introduction to the special section on intelligent systems for socially aware computing

Pervasive social context: Taxonomy and survey

Nontrivial landmark recommendation using geotagged photos

Exploring pattern-aware travel routes for trajectory search

Semantic trajectories: Mobility data computation and annotation

Connecting people through physical proximity and physical resources at a conference

Introduction to the special section on social computing, behavioral-cultural modeling, and prediction

Optimization-based influencing of village social networks in a counterinsurgency

Markov models of social dynamics: Theory and applications

Using qualitative reasoning for social simulation of crowds

Detecting changes in information diffusion patterns over social networks

Analysis of friendship network and its role in explaining obesity

Issue 2(Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems), March 2013

Issue 1(Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context), January 2013

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 3

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 2

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 1

Paraphrase acquisition via crowdsourcing and machine learning

Content Provider	ACM Digital Library
Author	Burrows, Steven Stein, Benno Potthast, Martin
Copyright Year	2013
Abstract	To paraphrase means to rewrite content while preserving the original meaning. Paraphrasing is important in fields such as text reuse in journalism, anonymizing work, and improving the quality of customer-written reviews. This article contributes to paraphrase acquisition and focuses on two aspects that are not addressed by current research: (1) acquisition via crowdsourcing, and (2) acquisition of passage-level samples. The challenge of the first aspect is automatic quality assurance; without such a means the crowdsourcing paradigm is not effective, and without crowdsourcing the creation of test corpora is unacceptably expensive for realistic order of magnitudes. The second aspect addresses the deficit that most of the previous work in generating and evaluating paraphrases has been conducted using sentence-level paraphrases or shorter; these short-sample analyses are limited in terms of application to plagiarism detection, for example. We present the Webis Crowd Paraphrase Corpus 2011 (Webis-CPC-11), which recently formed part of the PAN 2010 international plagiarism detection competition. This corpus comprises passage-level paraphrases with 4067 positive samples and 3792 negative samples that failed our criteria, using Amazon's Mechanical Turk for crowdsourcing. In this article, we review the lessons learned at PAN 2010, and explain in detail the method used to construct the corpus. The empirical contributions include machine learning experiments to explore if passage-level paraphrases can be identified in a two-class classification problem using paraphrase similarity features, and we find that a k-nearest-neighbor classifier can correctly distinguish between paraphrased and nonparaphrased samples with 0.980 precision at 0.523 recall. This result implies that just under half of our samples must be discarded (remaining 0.477 fraction), but our cost analysis shows that the automation we introduce results in a 18% financial saving and over 100 hours of time returned to the researchers when repeating a similar corpus design. On the other hand, when building an unrelated corpus requiring, say, 25% training data for the automated component, we show that the financial outcome is cost neutral, while still returning over 70 hours of time to the researchers. The work presented here is the first to join the paraphrasing and plagiarism communities.
Starting Page	1
Ending Page	21
Page Count	21
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/2483669.2483676
Volume Number	4
Issue Number	3
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-07-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Mechanical Turk Paraphrase generation Corpus Cost analysis Plagiarism
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Paraphrase acquisition via crowdsourcing and machine learning.

Paraphrase acquisition via crowdsourcing and machine learning

Rapid Low-Cost Virtual Human Bootstrapping via the Crowd

Distributional phrasal paraphrase generation for statistical machine translation

Multitechnique paraphrase alignment: A contribution to pinpointing sub-sentential paraphrases

Using targeted paraphrasing and monolingual crowdsourcing to improve translation

Plagiarism Detection Using Machine Learning-Based Paraphrase Recognizer

A Joyful Ode to Automatic Orchestration

Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection

Paraphrase acquisition via crowdsourcing and machine learning

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Paraphrase acquisition via crowdsourcing and machine learning.

Paraphrase acquisition via crowdsourcing and machine learning

Rapid Low-Cost Virtual Human Bootstrapping via the Crowd

Distributional phrasal paraphrase generation for statistical machine translation

Multitechnique paraphrase alignment: A contribution to pinpointing sub-sentential paraphrases

Using targeted paraphrasing and monolingual crowdsourcing to improve translation

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A Joyful Ode to Automatic Orchestration

Plagiarism Meets Paraphrasing: Insights for the Next Generation in Automatic Plagiarism Detection

Paraphrase acquisition via crowdsourcing and machine learning