Applications of data analysis on scholarly long documents

Banerjee, Bipasha; Ingram, William A.; Wu, Jian; Fox, Edward A.

doi:10.1109/bigdata55660.2022.10020935

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…An entire document might be divided into sections that must include Introduction and Conclusion for all academic disciplines and valid structure. Statistical-based NLP techniques or trained models are not ft for academic research documents since they contain domain-specifc jargon, and writing style may vary widely [37]. In addition, the machine learning approach for working with text in documents requires a corpus of labeled data that is difcult and expensive for widespread forms of document structure or content [2,37,38].…”

Section: Information Extraction From Large Documentsmentioning

confidence: 99%

“…Statistical-based NLP techniques or trained models are not ft for academic research documents since they contain domain-specifc jargon, and writing style may vary widely [37]. In addition, the machine learning approach for working with text in documents requires a corpus of labeled data that is difcult and expensive for widespread forms of document structure or content [2,37,38]. Te technique of information extraction from large documents is often preprocessed by building the document's representation from its constituent units, such as sentences or other symbolic tokens, which can be aggregated [38].…”

Section: Information Extraction From Large Documentsmentioning

confidence: 99%

“…Tis approach is helpful in maintaining the overall context and storing the content in a readable and searchable format for ease of extracting, recognizing, or manipulating parts or whole documents. Tis process of encoding documents along with their rich structural dependencies benefts from formal techniques than machine learning [2,[37][38][39].…”

Section: Information Extraction From Large Documentsmentioning

confidence: 99%

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Parsing of Research Documents into XML Using Formal Grammars

Iwashokun,

Ade-Ibijola

2024

Applied Computational Intelligence and Soft Computing

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Automatic information extraction of content and style format in paged documents is challenging. It requires the conversion of the original document into a granular level of details for which every document section and content is identifiable. This functionality or tool does not exist for any academic research document yet. In this paper, we present an automated process of parsing research paper documents into XML files using a formal method approach of context-free grammars (CFGs) and regular expressions (REGEXs) definable of a standard template. We created a tool for the algorithms to parse these documents into tree-like structures organized as XML files named research_XML (RX) parser. The RX tool performed the extraction of syntactic structure and semantic information of the document’s contents into XML files. These XML output files are lightweight, analyzable, query-able, and web interoperable. The RX tool has a success rate of 91% when evaluated on fifty varying research documents of 160 average pages and 8,004 total pages. The tool and test data are accessible on GitHub repo. The novelty of our process is specific to applying formal techniques for information extraction in structured multipaged documents and academic research documents thus advancing the research in automatic information extraction.

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Section: Information Extraction From Large Documentsmentioning

confidence: 99%

Section: Information Extraction From Large Documentsmentioning

confidence: 99%

Section: Information Extraction From Large Documentsmentioning

confidence: 99%

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Parsing of Research Documents into XML Using Formal Grammars

Iwashokun,

Ade-Ibijola

2024

Applied Computational Intelligence and Soft Computing

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“…BERT [20] and SciBERT [8] are some popular language models that uses transformers to perform various sequence tasks including classification. To obtain more tailored results, we can fine-tune base models [7]. We use language models for our chapter classification work.…”

Section: Services Backgroundmentioning

confidence: 99%

Integrated Digital Library System for Long Documents and their Elements

Chekuri,

Chandrasekar,

Banerjee

et al. 2023

2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL)

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We describe a next-generation integrated Digital Library (DL) system that addresses the numerous goals associated with long documents such as Electronic Theses and Dissertations (ETDs). Our extensible workflow-centric design supports a variety of users/personas (e.g., researchers, curators, and experimenters) who can benefit from improved access to ETDs and the content buried therein. Our approach leverages natural language processing, deep learning, information retrieval, and software engineering methods. The services cover ingesting, storing, curating, analyzing, detecting, extracting, classifying, summarizing, topic modeling, browsing, searching, retrieving, recommending, visualizing/reporting, and interacting with ETDs and derivative text/image-based elements/objects. Workflows connect the services and their APIs, along with UI-based access. We believe our approach can guide others to combine tailored user support, research, and education by way of extensible DLs. CCS CONCEPTS• Information systems → Digital libraries and archives; Information retrieval; Document representation; Retrieval tasks and goals.

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“It answers questions that I didn’t know I had”: PhD students’ evaluation of an information-sharing knowledge graph

Gardasevic,

Lamba

2024

DLP

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Purpose Interdisciplinary PhD programs can be challenging as the vital information needed by students may not be readily available; it is scattered across the university’s websites, while tacit knowledge can be obtained only by interacting with people. Hence, there is a need to develop a knowledge management model to create, query and maintain a knowledge repository for interdisciplinary students. This study aims to propose a knowledge graph containing information on critical categories (faculty, classes, dissertations, etc.) and their relationships, extracted from multiple sources, essential for interdisciplinary PhD students. This study evaluates the usability of a participatory-designed knowledge graph intended to facilitate information exchange and decision-making. Methodology The authors used data from multiple sources (such as university websites, faculty profiles, publication and dissertation metadata and crowdsourced data) to generate a knowledge graph in the Neo4J Bloom platform. The authors recruited 15 interdisciplinary PhD students using convenience sampling from the University of Hawaiʻi at Mānoa at various PhD stages to design and populate the knowledge graph. Next, the authors conducted a mixed methods study to perform its usability evaluation. First, the authors engaged the students in a participatory design workshop to identify relevant graph queries. Second, the authors conducted semi-structured interviews to determine the usability of the knowledge graph and rate the queries. Each interview was coded with structural and thematic codes and was further analyzed using sentiment analysis in R programming language. Findings The usability findings demonstrate that interaction with this knowledge graph benefits PhD students by notably reducing uncertainty and academic stress, particularly among newcomers. Knowledge graph supported them in decision-making, especially when choosing collaborators (e.g. supervisor or dissertation committee members) in an interdisciplinary setting. Key helpful features are related to exploring student–faculty networks, milestones tracking, rapid access to aggregated data and insights into crowdsourced fellow students’ activities. However, they showed concerns about crowdsourced data privacy and accessibility. Although participants expressed the need for more qualitative data in the graph, they noted it helped identify people to talk to about the topics of their interest. Originality The knowledge graph provides a solution to meet the personalized needs of doctoral researchers and has the potential to improve the information discovery and decision-making process substantially. It also includes the tacit knowledge exchange support missing from most current approaches, which is critical for this population and establishing interdisciplinary collaborations. This approach can be applied to other interdisciplinary programs and domains globally.

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Applications of data analysis on scholarly long documents

Cited by 3 publications

References 17 publications

Parsing of Research Documents into XML Using Formal Grammars

Parsing of Research Documents into XML Using Formal Grammars

Integrated Digital Library System for Long Documents and their Elements

“It answers questions that I didn’t know I had”: PhD students’ evaluation of an information-sharing knowledge graph

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