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Examining Citations of Natural Language Processing Literature
We extracted information from the ACL Anthology (AA) and Google Scholar ...
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Langsmith: An Interactive Academic Text Revision System
Despite the current diversity and inclusion initiatives in the academic ...
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Developmental tendencies in the Academic Field of Intellectual Property through the Identification of Invisible Colleges
The emergence of intellectual property as an academic issue opens a big ...
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Using Full-text Content of Academic Articles to Build a Methodology Taxonomy of Information Science in China
Research on the construction of traditional information science methodol...
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NLPExplorer: Exploring the Universe of NLP Papers
Understanding the current research trends, problems, and their innovativ...
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A Template and Suggestions for Writing Easy-to-Read Research Articles
The number of research papers written has been growing at least linearly...
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AI Marker-based Large-scale AI Literature Mining
The knowledge contained in academic literature is interesting to mine. I...
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Using the Full-text Content of Academic Articles to Identify and Evaluate Algorithm Entities in the Domain of Natural Language Processing
In the era of big data, the advancement, improvement, and application of algorithms in academic research have played an important role in promoting the development of different disciplines. Academic papers in various disciplines, especially computer science, contain a large number of algorithms. Identifying the algorithms from the full-text content of papers can determine popular or classical algorithms in a specific field and help scholars gain a comprehensive understanding of the algorithms and even the field. To this end, this article takes the field of natural language processing (NLP) as an example and identifies algorithms from academic papers in the field. A dictionary of algorithms is constructed by manually annotating the contents of papers, and sentences containing algorithms in the dictionary are extracted through dictionary-based matching. The number of articles mentioning an algorithm is used as an indicator to analyze the influence of that algorithm. Our results reveal the algorithm with the highest influence in NLP papers and show that classification algorithms represent the largest proportion among the high-impact algorithms. In addition, the evolution of the influence of algorithms reflects the changes in research tasks and topics in the field, and the changes in the influence of different algorithms show different trends. As a preliminary exploration, this paper conducts an analysis of the impact of algorithms mentioned in the academic text, and the results can be used as training data for the automatic extraction of large-scale algorithms in the future. The methodology in this paper is domain-independent and can be applied to other domains.
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