Jha N., Bhowmick P. K., & Bhagat K. (2024). Online Inquiry-based Learning Systems for Argumentation: A Systematic Review, 27(1), 1–28.
@article{article,
author = {Jha, Nitesh and Bhowmick, Plaban and Bhagat, Kaushal},
year = {2024},
month = {01},
pages = {1-28},
title = {Online inquiry-based learning systems for argumentation: A systematic review},
volume = {27},
journal = {Educational Technology & Society},
doi = {10.30191/ETS.202407_27(3).RP01}
}
The aim of this study is to provide a current synthesis of Online Inquiry-Based Learning (OIBL)
systems that use argumentation as a pedagogy. Data were collected from three databases: Scopus, Web of
Science, and ERIC. The present review synthesized the findings of 73 studies from 2010 to June 2023. A
qualitative content analysis was conducted to examine the inquiry-based systems regarding design features that
support argumentation and learning outcomes. Four design features were identified: engaging students in unique
and meaningful topics, providing visualization and scaffolding tools, collaborative inquiry in groups, and sharing
and critiquing arguments. Most studies provided scaffolding and visualization support, while a few allowed
students to engage in unique and meaningful topics. Most studies measured higher-level cognitive outcomes in
contrast to lower-level outcomes. Future studies need to design systems for a more diverse population of students
with improved collaboration support. In addition, this review identified a need to focus more on interdisciplinary
topics rather than natural science.
Acharyya S., Bhowmick P. K., & Guha P. K. (2023). Selective Identification and Quantification of VOCs using Metal Nanoparticles Decorated SnO2 Hollow-Spheres based Sensor Array and Machine Learning, Journal of Alloys and Compounds, 968(2), 1–14.
@article{ACHARYYA2023171891,
title = {Selective identification and quantification of VOCs using metal nanoparticles decorated SnO2 hollow-spheres based sensor array and machine learning},
journal = {Journal of Alloys and Compounds},
volume = {968},
pages = {171891},
year = {2023},
issn = {0925-8388},
doi = {https://doi.org/10.1016/j.jallcom.2023.171891},
url = {https://www.sciencedirect.com/science/article/pii/S0925838823031948},
author = {Snehanjan Acharyya and Plaban Kumar Bhowmick and Prasanta Kumar Guha},
keywords = {Selectivity, Metal-oxide, Chemiresistive gas sensor, Volatile organic compounds, Machine learning}
}
Accurate and selective detection of target gas/volatile organic compounds (VOCs) is of utmost importance. The chemiresistive gas sensors have been a desirable candidate due to their compact footprint and ease of fabrication, but they show poor selectivity. This work presents a combination of nanomaterials-based chemiresistive gas sensors with machine learning (ML) techniques to achieve sensitive, selective, and quantified detection of tested VOCs. The sensor array consists of four separate sensing layers over interdigitated electrodes-based platform. The sensing materials were comprised of silver, gold, palladium, and platinum nanoparticles decorated on tin oxide hollow-sphere structures which were successfully synthesized through chemical routes and characterized accordingly. Surface decoration of different metal nanoparticles has produced sensitive and diverse sensing patterns among the tested VOCs. The sensing mechanism and related gas sensing kinetics were then analyzed to explain high sensitivity and diverse sensing phenomena. The subsequent incorporation of ML models has resulted in qualitative and quantitative detection of VOCs. A comparative analysis was carried out among different types of applied features and ML models with reasoning. Particularly, a deep neural network (DNN) model with time series (TS) response sequence as input information, delivered the best performance. The DNN_TS model presented an average classification accuracy of 98.33 %, in conjunction with excellent concentration prediction. The DNN_TS model showed a very fast prediction time of 2.74 µs with adaptive learning while utilizing minimum computing resources, which favors the real-time sensing capability. The reported results promote the development of an autonomous, smart, and selective gas sensor system for real-time applications.
Singh D. K., Bhowmick P. K., & Paik J. H. (2023). Researcher Influence Prediction (ResIP) using Academic Genealogy Network, Journal of Informetrics, 17(2), 1–19.
@article{infometrics_23,
author = {Dhananjay Kumar and
Plaban Kumar Bhowmick and
Jiaul H. Paik},
title = {Researcher influence prediction (ResIP) using academic genealogy network},
journal = {J. Informetrics},
volume = {17},
number = {2},
pages = {101392},
year = {2023},
url = {https://doi.org/10.1016/j.joi.2023.101392},
doi = {10.1016/j.joi.2023.101392},
timestamp = {Thu, 15 Jun 2023 21:57:40 +0200},
biburl = {https://dblp.org/rec/journals/joi/KumarBP23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
In academia researchers join a research community over time and contribute to the advancement of a field in a variety of ways. One of the most established ways to contribute to the field is by passing on knowledge to the future generations through academic advising. Many academic scholars have more influence, while others fail to make an impact. Typically, academic influence refers to the ability of a researcher to pass on her/his “academic gene” in future researchers. In this article, we propose the task of Researcher Influence Prediction (ResIP) to predict researchers’ future influence in an academic field through the analysis of the corresponding academic genealogy network. Researcher influence prediction has got several implications as far as different academic outcomes are concerned (e.g. funding, awards, career progression, collaboration, identifying prolific researchers etc.).
To address the ResIP, a number of end-to-end deep learning architectures have been proposed in the current work. The proposed architectures take as input the lineage graph of a researcher at a given time point and predicts the growth of his/her family in future time points. The design of encoder in the proposed architecture considers both temporal and structural information of the input lineage graph while the decoders are tuned towards the nature of the output (single point vs. sequence). The proposed models have been trained, validated and compared with strong baselines using datasets created out of a subset of researchers from the Mathematics Genealogy Project (MGP).
Singh D. K., Bhowmick P. K., Dey S. & Sanyal D. K. (2023). On the banks of Shodhganga: analysis of the academic genealogy graph of an Indian ETD repository, Scientometrics, 128, 3879–3914.
@article{scientometrics_23,
author = {Dhananjay Kumar and
Plaban Kumar Bhowmick and
Sumana Dey and
Debarshi Kumar Sanyal},
title = {On the banks of Shodhganga: analysis of the academic genealogy graph
of an Indian {ETD} repository},
journal = {Scientometrics},
volume = {128},
number = {7},
pages = {3879--3914},
year = {2023},
url = {https://doi.org/10.1007/s11192-023-04728-z},
doi = {10.1007/s11192-023-04728-z},
timestamp = {Sun, 18 Jun 2023 10:49:32 +0200},
biburl = {https://dblp.org/rec/journals/scientometrics/KumarBDS23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Academic genealogy graphs capture information about the lineage of researchers, encode how knowledge flows from advisors to proteges, and shed light on the birth and evolution of disciplines. In this paper, we study the academic genealogy graph/network (AGN) in Shodhganga which is the Indian Electronic Theses and Dissertations (ETD) database. We have disambiguated the names of the researchers in Shodhganga and constructed the Shodhganga-AGN, which we have analyzed with topological metrics proposed in the literature on general graphs as well as that on genealogy networks. The metrics studied have been able to identify the institutes and researchers that have played a significant role in the development of the Indian higher education system. The largest connected component of Shodhganga-AGN consists of 1356 researchers and 1437 advisor–advisee relationships. The component is dominated by researchers from science and is affiliated primarily with three institutions. We have also studied subgraphs in the genealogy network to identify supervision patterns, and found that most of the subgraph instances connect researchers within a single institution or subject. Thus, our study is a detailed insightful analysis of the academic genealogy of researchers indexed in Shodhganga, and captures the decades-old research ecosystem of India, as expressed through the formal advisor–advisee relationships in Indian universities.
Singh P. K., & Bhowmick P. K. (2023). Semantics-Aware Query Expansion using Pseudo-Relevance Feedback , Journal of Information Science, 0(0), Accepted.
@article{jis_23,
author = {Pankaj Singh and Plaban Kumar Bhowmick},
title ={Semantics-aware query expansion using pseudo-relevance feedback},
journal = {Journal of Information Science},
volume = {0},
number = {0},
pages = {01655515231184831},
year = {0},
doi = {10.1177/01655515231184831},
}
In this article, a pseudo-relevance feedback (PRF)–based framework is presented for effective query expansion (QE). As candidate expansion terms, the proposed PRF framework considers the terms that are different morphological variants of the original query terms and are semantically close to them. This strategy of selecting expansion terms is expected to preserve the query intent after expansion. While judging the suitability of an expansion term with respect to a base query, two aspects of relation of the term with the query are considered. The first aspect probes to what extent the candidate term is semantically linked to the original query and the second one checks the extent to which the candidate term can supplement the base query terms. The semantic relationship between a query and expansion terms is modelled using bidirectional encoder representations from transformers (BERT). The degree of similarity is used to estimate the relative importance of the expansion terms with respect to the query. The quantified relative importance is used to assign weights of the expansion terms in the final query. Finally, the expansion terms are grouped into semantic clusters to strengthen the original query intent. A set of experiments was performed on three different Text REtrieval Conference (TREC) collections to experimentally validate the effectiveness of the proposed QE algorithm. The results show that the proposed QE approach yields competitive retrieval effectiveness over the existing state-of-the-art PRF methods in terms of the mean average precision (MAP) and precision P at position 10 (P@10).
Singh P. K., & Bhowmick P. K. (2022). Neural Network Guided Fast and Efficient Query-Based Stemming by Predicting Term Co-occurrence Statistics, SN Computer Science, 3(3), 198.
@article{DBLP:journals/sncs/SinghB22,
author = {Pankaj Singh and
Plaban Kumar Bhowmick},
title = {Neural Network Guided Fast and Efficient Query-Based Stemming by Predicting
Term Co-occurrence Statistics},
journal = {{SN} Comput. Sci.},
volume = {3},
number = {3},
pages = {198},
year = {2022},
url = {https://doi.org/10.1007/s42979-022-01081-5},
doi = {10.1007/s42979-022-01081-5},
timestamp = {Mon, 28 Aug 2023 21:39:33 +0200},
biburl = {https://dblp.org/rec/journals/sncs/SinghB22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
In information retrieval (IR) systems, stemming is used as a recall enhancing strategy to address the vocabulary mismatch problem arising due to morphological phenomena. Co-occurrence statistics-oriented query-based stemmers have demonstrated remarkable improvement in the retrieval effectiveness. This process involves computation of corpus co-occurrence statistics between different pairs of words to discover morphological variations. This computation, being performed online, has negative impact on the overall search efficiency given that the computation of the co-occurrence statistics is time demanding. The central objective of this work is to develop a faster but approximate method for estimating the co-occurrence of two given words in a corpus to address the efficiency shortcomings of the query-based stemmers. This work presents an empirical study to observe the effect of using predicated co-occurrence in query-based stemming. In particular, the proposed query expansion algorithm aims at predicting the pointwise mutual information (PMI) between a word pair using a ridge regression-based neural network to enhance the efficiency. The neural network model takes as input the word embeddings of a pair of words and learns to predict the PMI value. The predicted PMI value is then used to assign relative importance to the obtained morphological variations to be included in the final query. A set of experiments performed on three different TREC collections to experimentally validate the effectiveness and efficiency of proposed algorithm. The results show that the proposed stemming approach leads to a remarkable efficiency improvement over the existing query-based stemmers without significantly impeding the retrieval effectiveness.
Ghosh K., Nangi S. R., Kanchugantla Y., Rayapati P. G., Bhowmick P. K., & Goyal P. (2022). Augmenting Video Lectures: Identifying Off-topic Concepts and Linking to Relevant Video Lecture Segments, International Journal of Artificial Intelligence in Education, 32, 382–412.
@article{DBLP:journals/aiedu/GhoshNKRBG22,
author = {Krishnendu Ghosh and
Sharmila Reddy Nangi and
Yashasvi Kanchugantla and
Pavan Gopal Rayapati and
Plaban Kumar Bhowmick and
Pawan Goyal},
title = {Augmenting Video Lectures: Identifying Off-topic Concepts and Linking
to Relevant Video Lecture Segments},
journal = {Int. J. Artif. Intell. Educ.},
volume = {32},
number = {2},
pages = {382--412},
year = {2022},
url = {https://doi.org/10.1007/s40593-021-00257-z},
doi = {10.1007/s40593-021-00257-z},
timestamp = {Mon, 08 Aug 2022 21:24:07 +0200},
biburl = {https://dblp.org/rec/journals/aiedu/GhoshNKRBG22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Video lectures are considered as one of the primary media to cater good-quality educational content to the learners. The video lectures illustrate the course-relevant concepts with necessary details. However, they sometimes fail to offer a basic understanding of off-topic concepts. Such off-topic concepts may spawn cognitive overload among the learners if those concepts are not familiar to them. To address this issue, we present a video lecture augmentation system that identifies the off-topic concepts and links them to relevant video lecture segments to furnish a basic understanding of the concerned concepts. Our augmentation system segregated the video lectures by identifying topical shifts in the lectures using a word embedding-based technique. The video segments were indexed on the basis of the underlying concepts. Identification of off-topic concepts was performed by modeling inter-concept relations in a semantic space. For each off-topic concept, appropriate video segments were fetched and re-ranked such that the top-ranked video segment offers the most basic understanding of the target off-topic concept. The proposed augmentation system was deployed as a web-based learning platform. Performance of the constituent modules was measured by using a manually curated dataset consisting of six video courses from the National Programme on Technology Enhanced Learning (NPTEL) archive. Feedback from 12 research scholars was considered to assess the quality of augmentations and usability of the learning platform. Both system and human-based evaluation indicated that the recommended augmentations were able to offer a basic understanding of the concerned off-topic concepts.
Bhowmick P. K., Das P. P., Chakrabarti P. P. & Sanyal D. K. (2022). National digital library of India: democratizing education in India, Communications of the ACM, 65(2), 58-61.
@article{DBLP:journals/cacm/BhowmickDCS22,
author = {Plaban Kumar Bhowmick and
Partha Pratim Das and
Partha Pratim Chakrabarti and
Debarshi Kumar Sanyal},
title = {National digital library of India: democratizing education in India},
journal = {Commun. {ACM}},
volume = {65},
number = {11},
pages = {58--61},
year = {2022},
url = {https://doi.org/10.1145/3550480},
doi = {10.1145/3550480},
timestamp = {Mon, 07 Nov 2022 21:23:55 +0100},
biburl = {https://dblp.org/rec/journals/cacm/BhowmickDCS22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
India boasts of one of the youngest populations globally with an average age of 29.a A report published by the Federation of Indian Chambers of Commerce and Industry (FICCI) in 2013 estimated that to train this large young population, the country must build six new universities and 270 new colleges every month in the next 20 years.7 It was an impossible target! Or so it seemed until 2014 when IIT Kharagpur conceptualized the National Digital Library of India (NDLI; https://ndl.iitkgp.ac.in/) with an aim to bring equity of access to educational resources for every Indian through a single window access mechanism. NDLI, a project funded by the Ministry of Education, Government of India, is a meta-library—a portal that connects users to hundreds of libraries in India and abroad and provides more than 81 million forms of educational content, including books, lecture videos, research articles, and more in over 100 languages, including several vernaculars used throughout the country.
Sanyal D. K., Bhowmick P. K., & Das P. P. (2021). A review of author name disambiguation techniques for the PubMed bibliographic database, Journal of Informormation Science, 47(2).
@article{DBLP:journals/jis/SanyalBD21,
author = {Debarshi Kumar Sanyal and
Plaban Kumar Bhowmick and
Partha Pratim Das},
title = {A review of author name disambiguation techniques for the PubMed bibliographic
database},
journal = {J. Inf. Sci.},
volume = {47},
number = {2},
year = {2021},
url = {https://doi.org/10.1177/0165551519888605},
doi = {10.1177/0165551519888605},
timestamp = {Tue, 01 Jun 2021 09:59:33 +0200},
biburl = {https://dblp.org/rec/journals/jis/SanyalBD21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Author names in bibliographic databases often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. In this article, we focus on the research efforts targeted to disambiguate author names specifically in the PubMed bibliographic database. We believe this concentrated review will be useful to the research community because it discusses techniques applied to a very large real database that is actively used worldwide. We make a comprehensive survey of the existing author name disambiguation (AND) approaches that have been applied to the PubMed database: we organise the approaches into a taxonomy; describe the major characteristics of each approach including its performance, strengths, and limitations; and perform a comparative analysis of them. We also identify the datasets from PubMed that are publicly available for researchers to evaluate AND algorithms. Finally, we outline a few directions for future work.
Sahu A. & Bhowmick P. K. (2021). Feature Engineering and Ensemble-Based Approach for Improving Automatic Short-Answer Grading Performance, IEEE Transactions on Learning Technologies, 13(1), 77-90.
@article{DBLP:journals/tlt/SahuB20,
author = {Archana Sahu and
Plaban Kumar Bhowmick},
title = {Feature Engineering and Ensemble-Based Approach for Improving Automatic
Short-Answer Grading Performance},
journal = {{IEEE} Trans. Learn. Technol.},
volume = {13},
number = {1},
pages = {77--90},
year = {2020},
url = {https://doi.org/10.1109/TLT.2019.2897997},
doi = {10.1109/TLT.2019.2897997},
timestamp = {Thu, 09 Apr 2020 21:56:14 +0200},
biburl = {https://dblp.org/rec/journals/tlt/SahuB20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
In this paper, we studied different automatic short answer grading (ASAG) systems to provide a comprehensive view of the feature spaces explored by previous works. While the performance reported in previous works have been encouraging, systematic study of the features is lacking. Apart from providing systematic feature space exploration, we also presented ensemble methods that have been experimentally validated to exhibit significantly higher grading performance over the existing papers in almost all the datasets in ASAG domain. A comparative study over different features and regression models toward short-answer grading has been performed with respect to evaluation metrics used in evaluating ASAG. Apart from traditional text similarity based features like WordNet similarity, latent semantic analysis, and others, we have introduced novel features like topic models suited for short text, relevance feedback based features. An ensemble-based model has been built using a combination of different regression models with an approach based on stacked regression. The proposed ASAG has been tested on the University of North Texas dataset for the regression task, whereas in case of classification task, the student response analysis (SRA) based ScientsBank and Beetle corpus have been used for evaluation. The grading performance in case of ensemble-based ASAG is highly boosted from that exhibited by an individual regression model. Extensive experimentation has revealed that feature selection, introduction of novel features, and regressor stacking have been instrumental in achieving considerable improvement in performance over the existing methods in ASAG domain.
Sanyal D. K., Bhowmick P. K., Das P. P., Chattopadhyay S. , Santosh T. (2019). Enhancing access to scholarly publications with surrogate resources, Scientometrics, 121(2), 1129-1164.
@article{DBLP:journals/scientometrics/SanyalBDCS19,
author = {Debarshi Kumar Sanyal and
Plaban Kumar Bhowmick and
Partha Pratim Das and
Samiran Chattopadhyay and
T. Y. S. S. Santosh},
title = {Enhancing access to scholarly publications with surrogate resources},
journal = {Scientometrics},
volume = {121},
number = {2},
pages = {1129--1164},
year = {2019},
url = {https://doi.org/10.1007/s11192-019-03227-4},
doi = {10.1007/s11192-019-03227-4},
timestamp = {Mon, 26 Jun 2023 20:56:34 +0200},
biburl = {https://dblp.org/rec/journals/scientometrics/SanyalBDCS19.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Digital libraries containing scholarly publications are common today. They are an invaluable source of information to students, researchers, and practitioners. However, many digital libraries expose only the article metadata like title, author names, publication date, and the abstract for free; access to full-text requires access toll. Given that journal subscription charges are sometimes prohibitive, many important publications remain beyond the access of researchers, especially in developing countries. While open access publication solves this issue, the hard reality is that many research papers are not currently available for free reading or download. In this paper, we present a novel approach to alleviate this problem. We present a technique to retrieve open access surrogates of a scholarly article when the latter is unavailable freely in a digital library. Surrogates are articles semantically close to the original articles, written by the same author(s) and give valuable insights into the paper being searched for; they address the same or a very similar problem using the same or very similar techniques. Our focus on approximate matches of scholarly articles distinguishes our application from many academic search engines. We run it on a large corpus of computer science papers and compare the results with human judgment. Experimental results show that our tool can indeed identify relevant OA surrogates of access-restricted papers.
Bhowmick P. K., Mitra P., & Basu A. (2019). Do We Agree? Measuring Agreement on the Human judgments in Emotion Annotation of News Sentences, Cybernetics and Systems, 41(7), 469-488.
@article{DBLP:journals/cas/BhowmickMB10,
author = {Plaban Kumar Bhowmick and
Pabitra Mitra and
Anupam Basu},
title = {Do We Agree? Measuring Agreement on the Human judgments in Emotion
Annotation of News Sentences},
journal = {Cybern. Syst.},
volume = {41},
number = {7},
pages = {469--488},
year = {2010},
url = {https://doi.org/10.1080/01969722.2010.511525},
doi = {10.1080/01969722.2010.511525},
timestamp = {Thu, 13 Aug 2020 12:42:50 +0200},
biburl = {https://dblp.org/rec/journals/cas/BhowmickMB10.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
An emotional text may be judged to belong to multiple emotion categories because it may evoke different emotions with varying degrees of intensity. For emotion analysis of text in a supervised manner, it is required to annotate text corpus with emotion categories. Because emotion is a very subjective entity, producing reliable annotation is of prime requirement for developing a robust emotion analysis model, so it is wise to have the data set annotated by multiple human judges and generate an aggregated data set provided that the emotional responses provided by different annotators over the data set exhibit substantial agreement. In reality, multiple emotional responses for an emotional text are common. So, the data set is a multilabel one where a single data item may belong to more than one category simultaneously. This article presents a new agreement measure to compute interannotator reliability in multilabel annotation. The new reliability coefficient has been applied to measure the quality of an emotion text corpus. The procedure for generating aggregated data and some corpus cleaning techniques are also discussed.
Dasgupta T., Basu A. Bhowmick P. K., & Mitra P. (2010). A Framework for the Automatic Generation of Indian Sign Language, Journal of Intelligent Systems, 19(2), 125-144.
@article{DBLP:journals/jois/DasguptaBBM10,
author = {Tirthankar Dasgupta and
Anupam Basu and
Plaban Kumar Bhowmick and
Pabitra Mitra},
title = {A Framework for the Automatic Generation of Indian Sign Language},
journal = {J. Intell. Syst.},
volume = {19},
number = {2},
pages = {125--144},
year = {2010},
url = {https://doi.org/10.1515/JISYS.2010.19.2.125},
doi = {10.1515/JISYS.2010.19.2.125},
timestamp = {Wed, 17 Feb 2021 08:59:36 +0100},
biburl = {https://dblp.org/rec/journals/jois/DasguptaBBM10.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Patent
Ghose A., Pal A. , Dutta Choudhury A. , Chattopadhyay T. , Bhowmick P. K., & Chattopadhyay D. (2020). Internet of things (IoT) application development, US Patent Granted - US10628136B2
Maiti S., Mukherjee D. , & Bhowmick P. K. (2015). System and method for generating a plan to complete a task in computing environment, US Patent Granted - 9201692
Selected conference publications
Jha N. K., Bhowmick P. K., & Bhagat K. K. (2023). Usability Evaluation of an Online Inquiry-based Learning Platform for Computational Thinking (CT-ONLINQ), The 23rd IEEE International Conference on Advanced Learning Technologies, 182-186.
Santosh T.Y.S.S., Sanyal D.K., Bhowmick P.K., & Das P.P. (2021). Gazetteer-Guided Keyphrase Generation from Research Papers, The 25th Pacific-Asia Conference Advances in Knowledge Discovery and Data Mining (PAKDD), 655-667.
Banerjee S., Sanyal D.K., Chattopadhyay S., Bhowmick P.K., & Das P.P. (2021). Automatic Recognition of Learning Resource Category in a Digital Library, The Proceedings of the ACM/IEEE Joint Conference on Digital Libraries JCDL 2021, 289-290.
Santosh T., Sanyal D. K., Bhowmick P. K., Das P. P. (2020). SaSAKE: Syntax and Semantics Aware Keyphrase Extraction from Research Papers, The Proceedings of the 28th International Conference on Computational Linguistics, COLING 2020, 5372-5383.
Santosh T., Sanyal D. K., Bhowmick P. K., & Das P. P. (2020). DAKE: Document-Level Attention for Keyphrase Extraction, The Proceedings of the European Conference on Information Retrieval,ECIR 2020 , 392-401.
Banerjee S., Sanyal D. K., Chattopadhyay S., Bhowmick P. K., & Das P. P. (2020). Segmenting Scientific Abstracts into Discourse Categories: A Deep Learning-Based Approach for Sparse Labeled Data, The Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, JCDL 2020, 429-432.
Analysis of the Academic Genealogy of Education Halder K., Chattopadhyay A. , Sanyal D. K., Bhowmick P. K., Das P. P. By ACM/IEEE Joint Conference on Digital Libraries (JCDL 2020) 525-526 (2020)
Halder K., Chattopadhyay A., Sanyal D. K., Bhowmick P. K., & Das P. P. (2020). Analysis of the Academic Genealogy of Education, The Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, JCDL 2020, 525-526.
Learning to retrieve related resources in a bibliographic information network Anthony P., Bhowmick P.K. By Proceedings of the ACM/IEEE Joint Conference on Digital Libraries 2019-June 283-286 (2019)
Anthony P., & Bhowmick P. K. (2019). Learning to retrieve related resources in a bibliographic information network, The Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, JCDL 2019, 283-286.
Bhowmick P. K., Sadhu S. By 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019 213-226 (2019)
Sadhu S., & Bhowmick P. K. (2019). Metadata-Based Automatic Query Suggestion in Digital Library Using Pattern Mining, The Proceedings of the 21st International Conference on Asia-Pacific Digital Libraries, ICADL 2019 213-226.
Nangi S. R., Kanchugantla Y. , Rayapati P. G., & Bhowmick P. K. (2019). OffVid: A System for Linking Off-Topic Concepts to Topically Relevant Video Lecture Segments, The Proceedings of the IEEE International Conference on Advanced Learning Technologies, ICALT 2019, 37-41.
Automatic Segmentation and Semantic Annotation of Verbose Queries in Digital Library Bhowmick P. K. By 22nd International Conference on Theory and Practice of Digital Libraries 270-276
Sadhu S., & Bhowmick P. K. (2018). Automatic Segmentation and Semantic Annotation of Verbose Queries in Digital Library, The Proceedings of the 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2028 270-276
Ghosh K., Bhowmick P. K., & Goyal P.(2017). Using re-ranking to boost deep learning based community question retrieval, The Proceedings of the International Conference on Web Intelligence, WI 2017 2807-814.
Automatic Generation and Insertion of Assessment Items in Online Video Courses A. Krishna, P. K. Bhowmick, A. Sahu, K. Ghosh, S. Roy By 20th International Conference on Intelligent User Interfaces - (2014)
Krishna A., Bhowmick P. K., Sahu A. Ghosh K. & Roy S.(2014). Automatic Generation and Insertion of Assessment Items in Online Video Courses, The Proceedings of the 20th International Conference on Intelligent User Interfaces, IUI 2014.