@inproceedings{zuo2020querying,
    author={Zuo, Chaoyuan and Acharya, Narayan and Banerjee, Ritwik},
    title={{Querying Across Genres to Retrieve Research that Supports Medical Claims made in News}},
    booktitle = {Proceesings of the 2020 Conference on Empirical Methods in Natural Language Processing},
    publisher = {Association for Computational Linguistics},
    year      = {2020}
}

@inproceedings{zuo2019style,
    author={Zuo, Chaoyuan and Zhao, Yu and Banerjee, Ritwik},
    title={{Style Change Detection with Feed-forward Neural Networks}},
    booktitle = {CLEF 2019 Working Notes. Working Notes of CLEF 2019 - Conference and Labs of the Evaluation Forum},
    series    = {{CEUR} Workshop Proceedings},
    publisher = {CEUR-WS.org},
    editor    = {Cappellato, Linda and Ferro, Nicola and Losada, David E. and Müller, Henning},
    address   = {Lugano, Switzerland},
    month     = {September},
    year      = {2019}
}

The majority of previous authorship attribution studies mainly focus on a dataset of documents (or parts of documents) with labeled authorship. This scenario, however, is not applicable to documents written by more than one author. Detecting the authorship switches within multi-author documents has been shown to be a challenging task in previous PAN tasks. A simplified version of the style change task is thus organized by PAN 2019, which aims at identifying the number of authors in a given document. To this end, we present a system consisting of two modules, one for distinguishing the single-author documents from the multiauthor documents and the other for determining the exact number of authors in the multi-author documents.

@inproceedings{zuo2019check,
  author      = {Zuo, Chaoyuan and Karakas, Ayla and Banerjee, Ritwik},
  title       = {{To Check or not to Check: Syntax, Semantics, and Context in the Language of Check-worthy Claims}},
  editor      = {Crestani, Fabio and Braschler, Martin and Savoy, Jacques and Rauber, Andreas and Müller, Henning
                 and Losada, David E. and Bürki, Gundula H. and Cappellato, Linda and Ferro, Nicola},
  booktitle   = {Experimental IR Meets Multilinguality, Multimodality, and Interaction – Proceedings of the
                 10th International Conference of the CLEF Association},
  publisher   = {Springer International Publishing},
  year        = {2019},
  series      = {Lecture Notes in Computer Science},
  volume      = {11696}
}

As the spread of information has received a compelling boost due to pervasive use of social media, so has the spread of misinformation. The sheer volume of data has rendered the traditional methods of expert-driven manual fact-checking largely infeasible. As a result, computational linguistics and data-driven algorithms have been explored in recent years. Despite this progress, identifying and prioritizing what needs to be checked has received little attention. Given that expert-driven manual intervention is likely to remain an important component of fact-checking, especially in specific domains (e.g., politics, environmental science), this identification and prioritization is critical. A successful algorithmic ranking of "check-worthy" claims can help an expert-in-the-loop fact-checking system, thereby reducing the expert's workload while still tackling the most salient bits of misinformation. In this work, we explore how linguistic syntax, semantics, and the contextual meaning of words play a role in determining the check-worthiness of claims. Our preliminary experiments used explicit stylometric features and simple word embeddings on the English language dataset in the Check-worthiness task of the CLEF-2018 Fact-Checking Lab, where our primary solution outperformed the other systems in terms of the mean average precision, R-precision, reciprocal rank, and precision at k for multiple values of k. Here, we present an extension of this approach with more sophisticated word embeddings and report further improvements in this task.

@inproceedings{zuo2018hybrid,
  author={Zuo, Chaoyuan and Karakas, Ayla and Banerjee, Ritwik},
  title={{A Hybrid Recognition System for Check-worthy Claims Using Heuristics and Supervised Learning}},
  booktitle = {CLEF 2018 Working Notes. Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum},
    series    = {{CEUR} Workshop Proceedings},
    publisher = {CEUR-WS.org},
    editor    = {Cappellato, Linda and Ferro, Nicola and Nie, Jian-Yun and Soulier, Laure},
    address   = {Avignon, France},
    month     = {September},
    year      = {2018}
}

In recent years, the speed at which information disseminates has received an alarming boost from the pervasive usage of social media. To the detriment of political and social stability, this has also made it easier to quickly spread false claims. Due to the sheer volume of information, manual fact-checking seems infeasible, and as a result, computational approaches have been recently explored for automated fact-checking. In spite of the recent advancements in this direction, the critical step of recognizing and prioritizing statements worth fact-checking has received little attention. In this paper, we propose a hybrid approach that combines simple heuristics with supervised machine learning to identify claims made in political debates and speeches, and provide a mechanism to rank them in terms of their "check-worthiness". The viability of our method is demonstrated by evaluations on the English language dataset as part of the Check-worthiness task of the CLEF-2018 Fact Checking Lab.

@inproceedings{banerjee2015_ichi,
  author={Banerjee, Ritwik and Ramakrishnan, I. V. and Henry, Mark and Perciavalle, Matthew},
  booktitle={{Healthcare Informatics (ICHI), 2015 International Conference on}},
  title={{Patient Centered Identification, Attribution, and Ranking of Adverse Drug Events}},
  year={2015},
  pages={18--27},
  month={Oct},
  organization={IEEE}
}

Adverse drug events (ADEs) trigger a high number of hospital emergency room (ER) visits. Information about ADEs is often available in online drug databases in the form of narrative texts, and serves as the physician’s primary reference point for ADE attribution and diagnosis. Manually reviewing these narratives, however, is an error prone and time consuming process, especially due to the prevalence of polypharmacy. So ER healthcare providers, especially given the heavy volume of traffic in ERs, often either skip this step or at best do it rather perfunctorily. This causes ADEs to be missed or misdiagnosed, often leading to extensive and unnecessary testing and treatment, including hospitalization. In this paper, we present a system that automates the detection of ADEs and provides a list of suspect drugs, ranked by their likelihood of causing the patient’s complaints and symptoms. The input data, i.e., medications and complaints, are obtained from triage notes that often contain descriptive language. Our application utilizes heterogeneous information sources (including drug databases) to refine and transform these descriptions as well as the online database narratives using a natural language processing (NLP) pipeline. We then employ ranking measures to establish correspondence between the complaints and the medications. Our preliminary evaluation based on actual ER cases demonstrates that this system achieves high precision and recall.

@inproceedings{banerjee2015pam,
  author={Banerjee, Ritwik and Razaghpanah, Abbas and Chiang, Luis and Mishra, Akassh and Sekar, Vyas and Choi, Yejin and Gill, Phillipa},
  title={{Internet Outages, the Eyewitness Accounts: Analysis of the Outages Mailing List}},
  booktitle={{Passive and Active Measurement: 16th International Conference}},
  year={2015},
  volume={8995},
  pages={206--217},
  year={2015},
  organization={Springer}
}

Understanding network reliability and outages is critical to the "health" of the Internet infrastructure. Unfortunately, our ability to analyze Internet out- ages has been hampered by the lack of access to public information from key players. In this paper, we leverage a somewhat unconventional dataset to analyze Internet reliability—the outages mailing list. The mailing list is an avenue for network operators to share information and insights about widespread outages. Using this unique dataset, we perform a first-of-its-kind longitudinal analysis of Internet outages from 2006 to 2013 using text mining and natural language pro- cessing techniques. We observe several interesting aspects of Internet outages: a large number of application and mobility issues that impact users, a rise in con- tent, mobile issues, and discussion of large-scale DDoS attacks in recent years.

@phdthesis{banerjee2015_dissertation,
  title={{Knowledge Extraction from Diverse Biomedical Corpora with Applications in Healthcare: Bridging the Translational Gap}},
  author={Banerjee, Ritwik},
  year={2015},
  school={Stony Brook University},
  address={Stony Brook, NY},
  month={December},
}

@inproceedings{banerjee2014_emnlp,
  author={Banerjee, Ritwik and Feng, Song and Kang, Jun Seok and Choi, Yejin},
  title={{Keystroke Patterns as Prosody in Digital Writings: A Case Study with Deceptive Reviews and Essays}},
  booktitle={{Proceedings of the 2014 Conference on Empirical Methods on Natural Language Processing}},
  year={2014},
  pages={1469--1473},
  organization={Association for Computational Linguistics}
}

In this paper, we explore the use of keyboard strokes as a means to access the real-time writing process of online authors, analogously to prosody in speech analysis, in the context of deception detection. We show that differences in keystroke patterns like editing maneuvers and duration of pauses can help distinguish between truthful and deceptive writing. Empirical results show that incorporating keystroke-based features lead to improved performance in deception detection in two different domains: online reviews and essays.

The dataset contains truthful and deceptive writings from two domains: business reviews, and essays on two topics of social interest — 'gun control' and 'gay marriage'. The data is available for download as compressed tar.bz2 files:
— Restaurant Reviews
— Gun Control
— Gay Marriage
The uncompressed dataset consists of files with tab-separated values. The key log data is found in the last column, titled ReviewMeta. This field has a list of KeyUp, KeyDown and MouseUp event logs. Note that the first event timestamp is not always zero. The event logs have the following formats:
[timestamp] KeyUp/KeyDown [javascript keycode]
[timestamp] MouseUp [begin-index] [end-index]

@inproceedings{banerjee2014_ichi,
  author={Banerjee, Ritwik and Choi, Yejin and Piyush, Gaurav and Naik, Ameya and Rakamrishnan, I. V.},
  title={{Automated Suggestion of Laboratory Tests for Identifying Likelihood of Adverse Drug Events}},
  booktitle={{2014 International Conference on Healthcare Informatics}},
  year={2014},
  month={September},
  pages={170--175},
  organization={IEEE}
}

Adverse drug events (ADE) caused by use, misuse or sudden discontinuation of medications trigger hospital emergency room visits. Information about a wide range of drugs and associated ADEs is provided in online drug databases in the form of narrative texts. Even though some ADEs can be detected by observable symptoms, several others can only be confirmed by laboratory tests. In this paper, we present a system that provides automated suggestion of tests to identify the likelihood of ADEs. Given a patient’s medications and an optional list of signs and symptoms, our system automatically produces the laboratory tests needed to confirm possible ADEs associated with these drugs. The basis of our application is to map clinical symptoms to medical problems and laboratory tests. Towards that, we use template-based extraction and shallow parsing techniques from natural language processing to extract information from narrative texts. We employ relevance ranking measures to establish correspondence between the tests and ADEs. Our evaluation based on a sample set of 40 drugs shows that this system achieves relatively high sensitivity.

@inproceedings{feng2012_emnlp,
  title={{Characterizing Stylistic Elements in Syntactic Structure}},
  author={Feng, Song and Banerjee, Ritwik and Choi, Yejin},
  booktitle={{Proceedings of the 2012 Joint Conference on Empirical Methods on Natural Language Processing and
                    Computational Natural Language Learning}},
  pages={1522--1533},
  year={2012},
  organization={Association for Computational Linguistics}
}

Much of the writing styles recognized in rhetorical and composition theories involve deep syntactic elements. However, most previous research for computational stylometric analysis has relied on shallow lexico-syntactic patterns. Some very recent work has shown that PCFG models can detect distributional difference in syntactic styles, but without offering much insights into exactly what constitute salient stylistic elements in sentence structure characterizing each authorship. In this paper, we present a comprehensive exploration of syntactic elements in writing styles, with particular emphasis on interpretable characterization of stylistic elements. We present analytic insights with respect to the authorship attribution task in two different domains.

@inproceedings{feng2012_acl,
  title={{Syntactic Stylometry for Deception Detection}},
  author={Feng, Song and Banerjee, Ritwik and Choi, Yejin},
  booktitle={Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers-Volume 2},
  pages={171--175},
  year={2012},
  organization={Association for Computational Linguistics}
}

Most previous studies in computerized deception detection have relied only on shallow lexico-syntactic patterns. This paper investigates syntactic stylometry for deception detection, adding a somewhat unconventional angle to prior literature. Over four different datasets spanning from the product review to the essay domain, we demonstrate that features driven from Context Free Grammar (CFG) parse trees consistently improve the detection performance over several baselines that are based only on shallow lexico-syntactic features. Our results improve the best published result on the hotel review data [Ott et al., 2011] reaching 91.2% accuracy – a 14% error reduction.