Improving Performance of Automatic Keyword Extraction (AKE) Methods Using PoS Tagging and Enhanced Semantic-Awareness

Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process. It has various applications in information retrieval (IR) and natural language processing (NLP), including text summarisation, topic analysis and do...

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Vydané v:Information (Basel) Ročník 16; číslo 7; s. 601
Hlavní autori: Altuncu, Enes, Nurse, Jason R. C., Xu, Yang, Guo, Jie, Li, Shujun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.07.2025
Predmet:
Analysis
Artificial intelligence
Computational linguistics
Context
context-awareness
Datasets
Documents
Information retrieval
keyword extraction
Keywords
Language
Language processing
Machine learning
Methods
Microprocessors
Natural language interfaces
Natural language processing
Performance evaluation
pos tagging
semantic-awareness
Semantics
Technology application
United Kingdom
ISSN:2078-2489, 2078-2489
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Shrnutí:Automatic keyword extraction (AKE) has gained more importance with the increasing amount of digital textual data that modern computing systems process. It has various applications in information retrieval (IR) and natural language processing (NLP), including text summarisation, topic analysis and document indexing. This paper proposes a simple but effective post-processing-based universal approach to improving the performance of any AKE methods, via an enhanced level of semantic-awareness supported by PoS tagging. To demonstrate the performance of the proposed approach, we considered word types retrieved from a PoS tagging step and two representative sources of semantic information—specialised terms defined in one or more context-dependent thesauri, and named entities in Wikipedia. The above three steps can be simply added to the end of any AKE methods as part of a post-processor, which simply re-evaluates all candidate keywords following some context-specific and semantic-aware criteria. For five state-of-the-art (SOTA) AKE methods, our experimental results with 17 selected datasets showed that the proposed approach improved their performances both consistently (up to 100% in terms of improved cases) and significantly (between 10.2% and 53.8%, with an average of 25.8%, in terms of F1-score and across all five methods), especially when all the three enhancement steps are used. Our results have profound implications considering the fact that our proposed approach can be easily applied to any AKE method with the standard output (candidate keywords and scores) and the ease to further extend it.
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ISSN:2078-2489
2078-2489
DOI:10.3390/info16070601