Table Of ContentPrasenjit Majumder · Mandar Mitra
Parth Mehta · Jainisha Sankhavara (Eds.)
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FIRE 2016 International Workshop
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Revised Selected Papers
123
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Prasenjit Majumder Mandar Mitra
(cid:129)
Parth Mehta Jainisha Sankhavara (Eds.)
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Text Processing
FIRE 2016 International Workshop
–
Kolkata, India, December 7 10, 2016
Revised Selected Papers
123
Editors
Prasenjit Majumder ParthMehta
DAIICT DAIICT
Gujarat Gujarat
India India
MandarMitra Jainisha Sankhavara
Indian Statistical Institute DAIICT
Kolkata Gujarat
India India
ISSN 0302-9743 ISSN 1611-3349 (electronic)
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Preface
Wearehappytopresentthecurrenteditedvolumeon“AdvancesinTextProcessing.”
This volume comprises 16 papers from the seven tracks offered at FIRE 2016: Con-
sumer Health Information Search (CHIS), Detecting Paraphrases in Indian Languages
(DPIL), Information Extraction from Microblogs Posted During Disasters, Persian
Plagiarism Detection (PersianPlagDet), Personality Recognition in Source Code
(PR-SOCO), Shared Task on Mixed Script Information Retrieval (MSIR), and Shared
Task on Code Mix Entity Extraction in Indian Languages (CMEE-IL).
Persianlanguagetextprocessingwasasignificantinclusionthisyear,inadditionto
the major Indian languages. FIRE was enriched by including an annotated Persian
corpora and five papers. The next major contribution in this volume is on the DPIL
task. Data on four Indian languages were presented – Tamil, Malayalam, Hindi, and
Punjabi–andfourpapersontheselanguagesareincludedinthisvolume.Weincluded
two papers on CHIS and MSIR each. Moreover, included one paper for each of the
following tracks: the PR-SOCO, CMEE-IL, and Microblog track.
We invited top teams to submit their papers for this book and received 19 papers.
All submissions underwent a review process after which 16 papers were selected for
inclusion.Wewouldliketothankthetrackorganizers andreviewersforhelpingusin
the selection process with their insightful opinion and feedback.
November 2017 Prasenjit Majumder
Mandar Mitra
Parth Mehta
Jainisha Sankhavara
Organization
FIRE 2016, the Forum for Information Retrieval Evaluation, was organized
by the Indian Statistical Institute, Kolkata.
General Chairs
Mandar Mitra Indian Statistical Institute Kolkata, India
Prasenjit Majumder Dhirubhai Ambani Institute of ICT, India
Program Committee
Habibollah Asghari ICT research Institute, Iran
Somnath Banerjee Jadavpur University, India
Debasis Ganguly Dublin City University, Ireland
Saptarshi Ghosh Indian Institute of Technology Kharagpur, India
Kripabandhu Ghosh Indian Institute of Technology Kanpur, India
Mark Michael Hall Edge Hill University, UK
Ben Heuwing Universität Hildesheim, Germany
Gareth Jones Dublin City University, Ireland
Anand Kumar M. Amrita Vishwa Vidyapeetham, India
Sobha Lalitha AU-KBC Research Centre, India
Johannes Leveling Elsevier, Germany
Mihai Lupu Vienna University of Technology, Austria
Debapriyo Majumdar Indian Statistical Institute Kolkata, India
Thomas Mandl Universität Hildesheim, Germany
Parth Mehta Dhirubhai Ambani Institute of ICT, India
Henning Müller University of Applied Sciences Western Switzerland,
Switzerland
Jiaul Paik Indian Institute of Technology Kharagpur, India
Girish Palshikar Tata Research Development and Design Centre, India
Swapan K. Parui Indian Statistical Institute Kolkata, India
Paulo Quaresma Universidade de Evora, Portugal
Nitin Ramrakhiyani Tata Research Development and Design Centre, India
Pattabhi Rao AU-KBC Research Centre, India
Paolo Rosso Universitat Politècnica de València, Spain
Rishiraj Saha Roy Max Planck Institute for Informatics, Germany
Jainisha Sankhavara Dhirubhai Ambani Institute of ICT, India
Manjira Sinha Conduent Labs, India
Jerome White New York University, Abu Dhabi
David Zellhoefer Berlin State Library, Germany
Contents
PAN@FIRE: Overview of the PR-SOCO Track on Personality
Recognition in SOurce COde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Francisco Rangel, Fabio González, Felipe Restrepo,
Manuel Montes, and Paolo Rosso
Microblog Retrieval During Disasters: Comparative Evaluation
of IR Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Moumita Basu, Kripabandhu Ghosh, Somenath Das,
Somprakash Bandyopadhyay, and Saptarshi Ghosh
Overview of the Mixed Script Information Retrieval (MSIR)
at FIRE-2016 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Somnath Banerjee, Kunal Chakma, Sudip Kumar Naskar,
Amitava Das, Paolo Rosso, Sivaji Bandyopadhyay,
and Monojit Choudhury
From Vector Space Models to Vector Space Models of Semantics . . . . . . . . 50
H. B. Barathi Ganesh, M. Anand Kumar, and K. P. Soman
Algorithms and Corpora for Persian Plagiarism Detection:
Overview of PAN at FIRE 2016. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Habibollah Asghari, Salar Mohtaj, Omid Fatemi, Heshaam Faili,
Paolo Rosso, and Martin Potthast
Predicting Type of Obfuscation to Enhance Text Alignment Algorithms . . . . 80
Fatemeh Mashhadirajab and Mehrnoush Shamsfard
A Fast Multi-level Plagiarism Detection Method Based on Document
Embedding Representation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Erfaneh Gharavi, Hadi Veisi, Kayvan Bijari, and Kiarash Zahirnia
Plagiarism Detection Based on a Novel Trie-Based Approach. . . . . . . . . . . . 109
Alireza Talebpour, Mohammad Shirzadi Laskoukelayeh,
and Zahra Aminolroaya
Using Local Text Similarity in Pairwise Document Analysis
for Monolingual Plagiarism Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
Nava Ehsan and Azadeh Shakery
Shared Task on Detecting Paraphrases in Indian Languages (DPIL):
An Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
M. Anand Kumar, Shivkaran Singh, B. Kavirajan, and K. P. Soman
VIII Contents
Anuj@DPIL-FIRE2016: A Novel Paraphrase Detection Method in Hindi
Language Using Machine Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
Anuj Saini and Aayushi Verma
Learning to Detect Paraphrases in Indian Languages . . . . . . . . . . . . . . . . . . 153
Kamal Sarkar
Sentence Paraphrase Detection Using Classification Models. . . . . . . . . . . . . 166
Liuyang Tian, Hui Ning, Leilei Kong, Kaisheng Chen,
Haoliang Qi, and Zhongyuan Han
Feature Engineering and Characterization of Classifiers for Consumer
Health Information Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
D. Thenmozhi, P. Mirunalini, and Chandrabose Aravindan
Identification of Relevance and Support for Consumer Health Information. . . 197
Suresh Kumar Sanampudi and Naveen Kumar Laskari
Entity Extraction of Hindi-English and Tamil-English Code-Mixed Social
Media Text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
G. Remmiya Devi, P. V. Veena, M. Anand Kumar, and K. P. Soman
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
PAN@FIRE: Overview of the PR-SOCO Track
on Personality Recognition in SOurce COde
B
Francisco Rangel1( ), Fabio Gonza´lez2, Felipe Restrepo2, Manuel Montes3,
and Paolo Rosso4
1 Autoritas Consulting, Valencia, Spain
[email protected]
2 MindLab Research Group, Universidad Nacional de Colombia, Bogot´a, Colombia
{fagonzalezo,ferestrepoca}@unal.edu.co
3 INAOE, Puebla, Mexico
[email protected]
4 PRHLT Research Center, Universitat Polit`ecnica de Val`encia, Valencia, Spain
[email protected]
Abstract. Author profiling consists of predicting an author’s demo-
graphics(e.g.age,gender,personality)fromherwriting.Afteraddressing
at PAN@CLEF mainly age and gender identification, and also person-
ality recognition in Twitter (http://pan.webis.de/), in this PAN@FIRE
track on Personality Recognition from SOurce COde (PR-SOCO) we
have addressed the problem of predicting an author’s personality from
hersourcecode.Inthispaper,weanalyse48runssentby11participants.
GivenasetofsourcecodeswritteninJavabystudentswhoansweredalso
apersonalitytest,participantshadtopredictbigfivetraits.Resultshave
been evaluated with two complementary measures (RMSE and Pearson
product-moment correlation) that have allowed to identify whether sys-
tems with low error rates may work due to random chance. No matter
theapproach,opennessisthetraitthatallowedtoobtainthebestresults
for both measures.
· ·
Keywords: Personality recognition Source code Author profiling
1 Introduction
Personality influence most, if not all, of the human activities, such as the way
people write [6,25], interact with others, and the way people make decisions,
for instance in the case of developers the criteria they consider when selecting
a software project they want to participate [22], or the way they write and
structure their source code. Personality is defined along five traits using the
Big Five Theory [7], which is the most widely accepted in psychology. The five
traits are: extroversion (E), emotional stability/neuroticism (S), agreeableness
(A), conscientiousness (C), and openness to experience (O).
Personality recognition may have several practical applications, for example
to set up high performance teams. In software development, not only technical
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