Retrieval-Efficiency Trade-Off of Unsupervised Keyword Extraction
نویسندگان
چکیده
Efficiently identifying keyphrases that represent a given document is challenging task. In the last years, plethora of keyword detection approaches were proposed. These can be based on statistical (frequency-based) properties e.g., tokens, specialized neural language models, or graph-based structure derived from document. The methods computationally amongst most efficient ones, while maintaining retrieval performance. One main properties, common to methods, their immediate conversion token space into graphs, followed by subsequent processing. this paper, we explore novel unsupervised approach which merges parts in sequential form, prior construction graph. Further, leveraging personalized PageRank, considers frequencies such sub-phrases alongside lengths during node ranking, demonstrate state-of-the-art capabilities being up two orders magnitude faster than current detectors as YAKE and MultiPartiteRank. proposed method’s scalability was also demonstrated computing for biomedical corpus comprised 14 million documents less minute.
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ژورنال
عنوان ژورنال: Lecture Notes in Computer Science
سال: 2022
ISSN: ['1611-3349', '0302-9743']
DOI: https://doi.org/10.1007/978-3-031-18840-4_27