Keyword Extraction Using Particle Swarm Optimization

Sowmya, D R; Sheeba, J.I.

doi:10.1016/j.procs.2016.05.208

Cited by 3 publications

(2 citation statements)

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“…Experimental results show that the frequency-based keyword extraction algorithm has the best effect [9]. Sowmya's (2016) main work is to extract the keywords from a conversation using particle swarm optimization [10]. Chen et al (2019) proposed a new graphbased measure for keyword extraction by leveraging higher-order structural features (e.g., motifs) of a word co occurrence graph [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Naive Bayesian Automatic Classification of Railway Service Complaint Text Based on Eigenvalue Extraction

2019

Teh. vjesn.

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Railways have developed rapidly in China for several decades. The hardware of railways has already reached the world's leading level, but the level of service of these railways still has room for improvement. The railway management department receives a large number of passenger complaints every year and records them in text, which needs to be classified and analyzed. The text of railway complaints includes characteristics spanning wide business coverage, various events, serious colloquialisms, interference and useless information. When using the direct classification via traditional text categorization, the classification accuracy is low. The key to the automatic classification of such text lies in an eigenvalue extraction. The more accurate the eigenvalue extraction, the higher the accuracy of text classification. In this paper, the TF-IDF algorithm, TextRank algorithm and Word2vec algorithm are selected to extract text eigenvalues, and a railway complaint text classification method is constructed with a naive Bayesian classifier. The three types of eigenvalue extraction algorithms are compared. The TF-IDF algorithm, based on eigenvalue extraction, achieves the highest automatic text classification accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

Naive Bayesian Automatic Classification of Railway Service Complaint Text Based on Eigenvalue Extraction

2019

Teh. vjesn.

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“…In [9] automatic keyword extraction was performed very effectively with unsupervised graph-based keyword ranking. Keyword extraction from conversations using particle swarm optimization was shown to produce highly accurate query results [13].…”

Section: A Automatic Keyword Extraction Techniquesmentioning

confidence: 99%

Automatic intonation-based keyword extraction from academic discourse

Lezhenin

Diachkov

Lamtev

et al. 2018

Annals of Computer Science and Information Systems

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This paper examines the perspectives of intonation processing for automatic keyword extraction. Based on a discourse intonation model from D. Brazil, automatic tone pattern recognition in speech stream is performed. It is shown that automatic classification of tone patterns can be done using simple polynomials and correlation. The original software tool PitchKey-wordExtractor (PKE) was applied to academic discourse (on-line lectures) to extract keywords. The results were compared to the output of popular tools for speech analytics: VoiceBase and IBM Watson. All the records were processed also with Praat software and annotated by human experts. Experiments show that none of the automatic systems outperforms the others and PKE, VoiceBase and IBM Watson have the identical error rates with respect to human expert opinion. It motivates further research and supports the tendency to integrate intonation and, more generally, prosody processing in automatic keyword extraction.

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