DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature

Sharma, Abheesht; Chhablani, Gunjan; Pandey, Harshit; Patil, Rajaswa

doi:10.48550/arxiv.2107.01198

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Mohammad (2020c; 2020b) used the NLP Scholar dataset to examine citation patterns, the gender gap between female and male first-time authors, and n−gram distributions through interactive visualization. DRIFT (Sharma et al, 2021) tracks the changes in cs.CL, the computer science computational linguistics tag from arXiv, focusing on single-word terms and their word embeddings over time. In NLPExplorer, (Parmar et al, 2020) provide an exploratory tool for NLP publications based on ACL Anthology, including information on most-cited authors, areas, and venues; similar to NLP Scholar.…”

Section: Existing Resourcesmentioning

confidence: 99%

D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research

Wahle¹,

Ruas²,

Mohammad³

et al. 2022

Preprint

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DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to identify trends in research activity, productivity, focus, bias, accessibility, and impact of computer science research. We present an initial analysis focused on the volume of computer science research (e.g., number of papers, authors, research activity), trends in topics of interest, and citation patterns. Our findings show that computer science is a growing research field (≈15% annually), with an active and collaborative researcher community. While papers in recent years present more bibliographical entries in comparison to previous decades, the average number of citations has been declining. Investigating papers' abstracts reveals that recent topic trends are clearly reflected in D3. Finally, we list further applications of D3 and pose supplemental research questions. The D3 dataset, our findings, and source code are publicly available for research purposes.

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Section: Existing Resourcesmentioning

confidence: 99%

D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research

Wahle¹,

Ruas²,

Mohammad³

et al. 2022

Preprint

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“…For massive analysis reports on the field of NLP, Mohammad (2020) surveyed the literature with 1.1 million paper information dataset collected from Google Scholar. Additionally, Sharma et al (2021) proposed DRIFT, a data analysis tool that presents an overview of the landscape of a queried topic. They constructed their dataset from arXiv papers' abstracts.…”

Section: Related Workmentioning

confidence: 99%

Masader: Metadata Sourcing for Arabic Text and Speech Data Resources

Alyafeai¹,

Masoud²,

Ghaleb³

et al. 2021

Preprint

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The NLP pipeline has evolved dramatically in the last few years. The first step in the pipeline is to find suitable annotated datasets to evaluate the tasks we are trying to solve. Unfortunately, most of the published datasets lack metadata annotations that describe their attributes. Not to mention, the absence of a public catalogue that indexes all the publicly available datasets related to specific regions or languages. When we consider low-resource dialectical languages, for example, this issue becomes more prominent. In this paper we create Masader, the largest public catalogue for Arabic NLP datasets, which consists of 200 datasets annotated with 25 attributes. Furthermore, We develop a metadata annotation strategy that could be extended to other languages. We also make remarks and highlight some issues about the current status of Arabic NLP datasets and suggest recommendations to address them.

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“…The global aim of this series of studies was to investigate the speech and natural language processing (SNLP), research area through the related scientific publications, using a set of NLP tools, in harmony with the growing interest for scientometrics in SNLP [refer to Banchs, 2012 ; Jurafsky, 2016 ; Atanassova et al, 2019 ; Goh and Lepage, 2019 ; Mohammad, 2020a , b , c ; Wang et al, 2020 ; Sharma et al, 2021 and many more] or in various domains such as economics (Muñoz-Céspedes et al, 2021 ), finance (Daudert and Ahmadi, 2019 ), or disinformation (Monogarova et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

NLP4NLP+5: The Deep (R)evolution in Speech and Language Processing

Mariani

Francopoulo²,

Paroubek³

et al. 2022

Front. Res. Metr. Anal.

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This paper aims at analyzing the changes in the fields of speech and natural language processing over the recent past 5 years (2016–2020). It is in continuation of a series of two papers that we published in 2019 on the analysis of the NLP4NLP corpus, which contained articles published in 34 major conferences and journals in the field of speech and natural language processing, over a period of 50 years (1965–2015), and analyzed with the methods developed in the field of NLP, hence its name. The extended NLP4NLP+5 corpus now covers 55 years, comprising close to 90,000 documents [+30% compared with NLP4NLP: as many articles have been published in the single year 2020 than over the first 25 years (1965–1989)], 67,000 authors (+40%), 590,000 references (+80%), and approximately 380 million words (+40%). These analyses are conducted globally or comparatively among sources and also with the general scientific literature, with a focus on the past 5 years. It concludes in identifying profound changes in research topics as well as in the emergence of a new generation of authors and the appearance of new publications around artificial intelligence, neural networks, machine learning, and word embedding.

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DRIFT: A Toolkit for Diachronic Analysis of Scientific Literature

Cited by 3 publications

References 8 publications

D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research

D3: A Massive Dataset of Scholarly Metadata for Analyzing the State of Computer Science Research

Masader: Metadata Sourcing for Arabic Text and Speech Data Resources

NLP4NLP+5: The Deep (R)evolution in Speech and Language Processing

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