A Review of Unsupervised Keyphrase Extraction Methods Using Within-Collection Resources

Sun, Chengyu; Hu, Liang; Li, Shuai; Li, Tuohang; Li, Hongtu; Chi, Ling

doi:10.3390/sym12111864

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…In addition, the use and identification of stopwords and custom_stopwords is essential and there are several alternative approaches to determine them ( Gerlach et al, 2019 ; Sarica and Luo, 2021 ). Similarly, there are additional algorithms for keyword and keyphrase extraction and sentiment analyses not used in this study ( Siddiqi and Sharan, 2015 ; Thelwall, 2017 ; Papagiannopoulou and Tsoumakas, 2020 ; Sun et al, 2020 ). The strategies used here were based on simplicity and the nature of the short text available for analyses in tweets, although we note more complex approaches are available for their analyses ( Edo-Osagie et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Twitter trends in #Parasitology determined by text mining and topic modelling

Ellis

Reichel

2023

Current Research in Parasitology & Vector-Borne Diseases

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Section: Discussionmentioning

confidence: 99%

Twitter trends in #Parasitology determined by text mining and topic modelling

Ellis

Reichel

2023

Current Research in Parasitology & Vector-Borne Diseases

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“…e relation between five features can be expressed through equation (1), where S(w) is the measure for each word. After calculating the measure for each word, the final keyword is calculated utilizing a 3-gram model [15]:…”

Section: Unsupervised Keyword Extractionmentioning

confidence: 99%

Study of Keyword Extraction Techniques for Electric Double‐Layer Capacitor Domain Using Text Similarity Indexes: An Experimental Analysis

et al. 2021

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Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert-provided keywords from the electric double layer capacitor (EDLC) domain. The paper also analyses which texts provide better keywords such as positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert-provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques.

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“…Before the emergence of technology, this information could be processed by humans, which was very time-consuming. Furthermore, due to the inconsistencies between the amount of data and manual data processing skills, it is challenging to complete this vast information, leading to automated keyphrase extraction systems that utilise computers' extensive computational capability to substitute manual labour [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

Miah¹,

Awang²,

Azad³

et al. 2022

IJACSA

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The extraction of high-quality keywords and summarising documents at a high level has become more difficult in current research due to technological advancements and the exponential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a highlevel need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach's effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains.

show abstract

A Review of Unsupervised Keyphrase Extraction Methods Using Within-Collection Resources

Cited by 21 publications

References 32 publications

Twitter trends in #Parasitology determined by text mining and topic modelling

Twitter trends in #Parasitology determined by text mining and topic modelling

Study of Keyword Extraction Techniques for Electric Double‐Layer Capacitor Domain Using Text Similarity Indexes: An Experimental Analysis

Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

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