2,450 research outputs found
MSIR@FIRE: A Comprehensive Report from 2013 to 2016
[EN] India is a nation of geographical and cultural diversity where over 1600 dialects are spoken by the people. With the technological advancement, penetration of the internet and cheaper access to mobile data, India has recently seen a sudden growth
of internet users. These Indian internet users generate contents either in English or in other vernacular Indian languages.
To develop technological solutions for the contents generated by the Indian users using the Indian languages, the Forum
for Information Retrieval Evaluation (FIRE) was established and held for the first time in 2008. Although Indian languages
are written using indigenous scripts, often websites and user-generated content (such as tweets and blogs) in these Indian
languages are written using Roman script due to various socio-cultural and technological reasons. A challenge that search
engines face while processing transliterated queries and documents is that of extensive spelling variation. MSIR track was
first introduced in 2013 at FIRE and the aim of MSIR was to systematically formalize several research problems that one must
solve to tackle the code mixing in Web search for users of many languages around the world, develop related data sets, test
benches and most importantly, build a research community focusing on this important problem that has received very little attention. This document is a comprehensive report on the 4 years of MSIR track evaluated at FIRE between 2013 and 2016.Somnath Banerjee and Sudip Kumar Naskar are supported by Media Lab Asia, MeitY, Government of India, under the Visvesvaraya PhD Scheme for Electronics & IT. The work of Paolo Rosso was partially supported by the MISMIS research project PGC2018-096212-B-C31 funded by the Spanish MICINN.Banerjee, S.; Choudhury, M.; Chakma, K.; Kumar Naskar, S.; Das, A.; Bandyopadhyay, S.; Rosso, P. (2020). MSIR@FIRE: A Comprehensive Report from 2013 to 2016. SN Computer Science. 1(55):1-15. https://doi.org/10.1007/s42979-019-0058-0S115155Ahmed UZ, Bali K, Choudhury M, Sowmya VB. Challenges in designing input method editors for Indian languages: the role of word-origin and context. In: Advances in text input methods (WTIM 2011). 2011. pp. 1–9Banerjee S, Chakma K, Naskar SK, Das A, Rosso P, Bandyopadhyay S, Choudhury M. Overview of the mixed script information retrieval (MSIR) at fire-2016. In: Forum for information retrieval evaluation. Springer; 2016. pp. 39–49.Banerjee S, Kuila A, Roy A, Naskar SK, Rosso P, Bandyopadhyay S. A hybrid approach for transliterated word-level language identification: CRF with post-processing heuristics. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 54–59.Banerjee S, Naskar S, Rosso P, Bandyopadhyay S. Code mixed cross script factoid question classification—a deep learning approach. J Intell Fuzzy Syst. 2018;34(5):2959–69.Banerjee S, Naskar SK, Rosso P, Bandyopadhyay S. The first cross-script code-mixed question answering corpus. In: Proceedings of the workshop on modeling, learning and mining for cross/multilinguality (MultiLingMine 2016), co-located with the 38th European Conference on Information Retrieval (ECIR). 2016.Banerjee S, Naskar SK, Rosso P, Bandyopadhyay S. Named entity recognition on code-mixed cross-script social media content. Comput Sistemas. 2017;21(4):681–92.Barman U, Das A, Wagner J, Foster J. Code mixing: a challenge for language identification in the language of social media. In: Proceedings of the first workshop on computational approaches to code switching. 2014. pp. 13–23.Bhardwaj P, Pakray P, Bajpeyee V, Taneja A. Information retrieval on code-mixed Hindi–English tweets. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.Bhargava R, Khandelwal S, Bhatia A, Sharmai Y. Modeling classifier for code mixed cross script questions. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Bhattacharjee D, Bhattacharya, P. Ensemble classifier based approach for code-mixed cross-script question classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Chakma K, Das A. CMIR: a corpus for evaluation of code mixed information retrieval of Hindi–English tweets. In: The 17th international conference on intelligent text processing and computational linguistics (CICLING). 2016.Choudhury M, Chittaranjan G, Gupta P, Das A. Overview of fire 2014 track on transliterated search. Proceedings of FIRE. 2014. pp. 68–89.Ganguly D, Pal S, Jones GJ. Dcu@fire-2014: fuzzy queries with rule-based normalization for mixed script information retrieval. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 80–85.Gella S, Sharma J, Bali K. Query word labeling and back transliteration for Indian languages: shared task system description. FIRE Working Notes. 2013;3.Gupta DK, Kumar S, Ekbal A. Machine learning approach for language identification and transliteration. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 60–64.Gupta P, Bali K, Banchs RE, Choudhury M, Rosso P. Query expansion for mixed-script information retrieval. In: Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, ACM, 2014. pp. 677–686.Gupta P, Rosso P, Banchs RE. Encoding transliteration variation through dimensionality reduction: fire shared task on transliterated search. In: Fifth forum for information retrieval evaluation. 2013.HB Barathi Ganesh, M Anand Kumar, KP Soman. Distributional semantic representation for information retrieval. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.HB Barathi Ganesh, M Anand Kumar, KP Soman. Distributional semantic representation for text classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Järvelin K, Kekäläinen J. Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst. 2002;20:422–46. https://doi.org/10.1145/582415.582418.Joshi H, Bhatt A, Patel H. Transliterated search using syllabification approach. In: Forum for information retrieval evaluation. 2013.King B, Abney S. Labeling the languages of words in mixed-language documents using weakly supervised methods. In: Proceedings of NAACL-HLT, 2013. pp. 1110–1119.Londhe N, Srihari RK. Exploiting named entity mentions towards code mixed IR: working notes for the UB system submission for MSIR@FIRE’16. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.Anand Kumar M, Soman KP. Amrita-CEN@MSIR-FIRE2016: Code-mixed question classification using BoWs and RNN embeddings. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Majumder G, Pakray P. NLP-NITMZ@MSIR 2016 system for code-mixed cross-script question classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Mandal S, Banerjee S, Naskar SK, Rosso P, Bandyopadhyay S. Adaptive voting in multiple classifier systems for word level language identification. In: FIRE workshops, 2015. pp. 47–50.Mukherjee A, Ravi A , Datta K. Mixed-script query labelling using supervised learning and ad hoc retrieval using sub word indexing. In: Proceedings of the Forum for Information Retrieval Evaluation, Bangalore, India, 2014.Pakray P, Bhaskar P. Transliterated search system for Indian languages. In: Pre-proceedings of the 5th FIRE-2013 workshop, forum for information retrieval evaluation (FIRE). 2013.Patel S, Desai V. Liga and syllabification approach for language identification and back transliteration: a shared task report by da-iict. In: Proceedings of the forum for information retrieval evaluation, ACM, 2014. pp. 43–47.Prabhakar DK, Pal S. Ism@fire-2013 shared task on transliterated search. In: Post-Proceedings of the 4th and 5th workshops of the forum for information retrieval evaluation, ACM, 2013. p. 17.Prabhakar DK, Pal S. Ism@ fire-2015: mixed script information retrieval. In: FIRE workshops. 2015. pp. 55–58.Prakash A, Saha SK. A relevance feedback based approach for mixed script transliterated text search: shared task report by bit Mesra. In: Proceedings of the Forum for Information Retrieval Evaluation, Bangalore, India, 2014.Raj A, Karfa S. A list-searching based approach for language identification in bilingual text: shared task report by asterisk. In: Working notes of the shared task on transliterated search at forum for information retrieval evaluation FIRE’14. 2014.Roy RS, Choudhury M, Majumder P, Agarwal K. Overview of the fire 2013 track on transliterated search. In: Post-proceedings of the 4th and 5th workshops of the forum for information retrieval evaluation, ACM, 2013. p. 4.Saini A. Code mixed cross script question classification. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. CEUR-WS.org. 2016.Salton G, McGill MJ. Introduction to modern information retrieval. New York: McGraw-Hill, Inc.; 1986.Sequiera R, Choudhury M, Gupta P, Rosso P, Kumar S, Banerjee S, Naskar SK, Bandyopadhyay S, Chittaranjan G, Das A, et al. Overview of fire-2015 shared task on mixed script information retrieval. FIRE Workshops. 2015;1587:19–25.Singh S, M Anand Kumar, KP Soman. CEN@Amrita: information retrieval on code mixed Hindi–English tweets using vector space models. In: Working notes of FIRE 2016—forum for information retrieval evaluation, Kolkata, India, December 7–10, 2016, CEUR workshop proceedings. 2016.Sinha N, Srinivasa G. Hindi–English language identification, named entity recognition and back transliteration: shared task system description. In: Working notes os shared task on transliterated search at forum for information retrieval evaluation FIRE’14. 2014.Voorhees EM, Tice DM. The TREC-8 question answering track evaluation. In: TREC-8, 1999. pp. 83–105.Vyas Y, Gella S, Sharma J, Bali K, Choudhury M. Pos tagging of English–Hindi code-mixed social media content. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014. pp. 974–979
A Comprehensive Review of Sentiment Analysis on Indian Regional Languages: Techniques, Challenges, and Trends
Sentiment analysis (SA) is the process of understanding emotion within a text. It helps identify the opinion, attitude, and tone of a text categorizing it into positive, negative, or neutral. SA is frequently used today as more and more people get a chance to put out their thoughts due to the advent of social media. Sentiment analysis benefits industries around the globe, like finance, advertising, marketing, travel, hospitality, etc. Although the majority of work done in this field is on global languages like English, in recent years, the importance of SA in local languages has also been widely recognized. This has led to considerable research in the analysis of Indian regional languages. This paper comprehensively reviews SA in the following major Indian Regional languages: Marathi, Hindi, Tamil, Telugu, Malayalam, Bengali, Gujarati, and Urdu. Furthermore, this paper presents techniques, challenges, findings, recent research trends, and future scope for enhancing results accuracy
Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph
As the security landscape evolves over time, where thousands of species of
malicious codes are seen every day, antivirus vendors strive to detect and
classify malware families for efficient and effective responses against malware
campaigns. To enrich this effort, and by capitalizing on ideas from the social
network analysis domain, we build a tool that can help classify malware
families using features driven from the graph structure of their system calls.
To achieve that, we first construct a system call graph that consists of system
calls found in the execution of the individual malware families. To explore
distinguishing features of various malware species, we study social network
properties as applied to the call graph, including the degree distribution,
degree centrality, average distance, clustering coefficient, network density,
and component ratio. We utilize features driven from those properties to build
a classifier for malware families. Our experimental results show that
influence-based graph metrics such as the degree centrality are effective for
classifying malware, whereas the general structural metrics of malware are less
effective for classifying malware. Our experiments demonstrate that the
proposed system performs well in detecting and classifying malware families
within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201
Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach
[EN] Before the advent of the Internet era, code-mixing was mainly used in the spoken form. However, with the recent popular informal networking platforms such as Facebook, Twitter, Instagram, etc., in social media, code-mixing is being used more and more in written form. User-generated social media content is becoming an increasingly important resource in applied linguistics. Recent trends in social media usage have led to a proliferation of studies on social media content. Multilingual social media users often write native language content in non-native script (cross-script). Recently Banerjee et al. [9] introduced the code-mixed cross-script question answering research problem and reported that the ever increasing social media content could serve as a potential digital resource for less-computerized languages to build question answering systems. Question classification is a core task in question answering in which questions are assigned a class or a number of classes which denote the expected answer type(s). In this research work, we address the question classification task as part of the code-mixed cross-script question answering research problem. We combine deep learning framework with feature engineering to address the question classification task and enhance the state-of-the-art question classification accuracy by over 4% for code-mixed cross-script questions.The work of the third author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project.Banerjee, S.; Kumar Naskar, S.; Rosso, P.; Bandyopadhyay, S. (2018). Code Mixed Cross Script Factoid Question Classification - A Deep Learning Approach. Journal of Intelligent & Fuzzy Systems. 34(5):2959-2969. https://doi.org/10.3233/JIFS-169481S2959296934
How Unique is Your .onion? An Analysis of the Fingerprintability of Tor Onion Services
Recent studies have shown that Tor onion (hidden) service websites are
particularly vulnerable to website fingerprinting attacks due to their limited
number and sensitive nature. In this work we present a multi-level feature
analysis of onion site fingerprintability, considering three state-of-the-art
website fingerprinting methods and 482 Tor onion services, making this the
largest analysis of this kind completed on onion services to date.
Prior studies typically report average performance results for a given
website fingerprinting method or countermeasure. We investigate which sites are
more or less vulnerable to fingerprinting and which features make them so. We
find that there is a high variability in the rate at which sites are classified
(and misclassified) by these attacks, implying that average performance figures
may not be informative of the risks that website fingerprinting attacks pose to
particular sites.
We analyze the features exploited by the different website fingerprinting
methods and discuss what makes onion service sites more or less easily
identifiable, both in terms of their traffic traces as well as their webpage
design. We study misclassifications to understand how onion service sites can
be redesigned to be less vulnerable to website fingerprinting attacks. Our
results also inform the design of website fingerprinting countermeasures and
their evaluation considering disparate impact across sites.Comment: Accepted by ACM CCS 201
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Perspective Identification in Informal Text
This dissertation studies the problem of identifying the ideological perspective of people as expressed in their written text. One's perspective is often expressed in his/her stance towards polarizing topics. We are interested in studying how nuanced linguistic cues can be used to identify the perspective of a person in informal genres. Moreover, we are interested in exploring the problem from a multilingual perspective comparing and contrasting linguistics devices used in both English informal genres datasets discussing American ideological issues and Arabic discussion fora posts related to Egyptian politics. %In doing so, we solve several challenges.
Our first and utmost goal is building computational systems that can successfully identify the perspective from which a given informal text is written while studying what linguistic cues work best for each language and drawing insights into the similarities and differences between the notion of perspective in both studied languages. We build computational systems that can successfully identify the stance of a person in English informal text that deal with different topics that are determined by one's perspective, such as legalization of abortion, feminist movement, gay and gun rights; additionally, we are able to identify a more general notion of perspective–namely the 2012 choice of presidential candidate–as well as build systems for automatically identifying different elements of a person's perspective given an Egyptian discussion forum comment. The systems utilize several lexical and semantic features for both languages. Specifically, for English we explore the use of word sense disambiguation, opinion features, latent and frame semantics as well; as Linguistic Inquiry and Word Count features; in Arabic, however, in addition to using sentiment and latent semantics, we study whether linguistic code-switching (LCS) between the standard and dialectal forms for the language can help as a cue for uncovering the perspective from which a comment was written.
This leads us to the challenge of devising computational systems that can handle LCS in Arabic. The Arabic language has a diglossic nature where the standard form of the language (MSA) coexists with the regional dialects (DA) corresponding to the native mother tongue of Arabic speakers in different parts of the Arab world. DA is ubiquitously prevalent in written informal genres and in most cases it is code-switched with MSA. The presence of code-switching degrades the performance of almost any MSA-only trained Natural Language Processing tool when applied to DA or to code-switched MSA-DA content. In order to solve this challenge, we build a state-of-the-art system–AIDA–to computationally handle token and sentence-level code-switching.
On a conceptual level, for handling and processing Egyptian ideological perspectives, we note the lack of a taxonomy for the most common perspectives among Egyptians and the lack of corresponding annotated corpora. In solving this challenge, we develop a taxonomy for the most common community perspectives among Egyptians and use an iterative feedback-loop process to devise guidelines on how to successfully annotate a given online discussion forum post with different elements of a person's perspective. Using the proposed taxonomy and annotation guidelines, we annotate a large set of Egyptian discussion fora posts to identify a comment's perspective as conveyed in the priority expressed by the comment, as well as the stance on major political entities
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