Automatic Keyphrases Extraction from Document Using Neural Network

Wang, Jiabing; Peng, Hong; Hu, Jinglu

doi:10.1007/11739685_66

Cited by 29 publications

(16 citation statements)

References 18 publications

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“…Depending on the weights the words that have higher score than the threshold will be selected as keyphrases. Wang J. et al [5] proposed in their paper Neural Network based keyphrase extraction method. Lui Y. J.…”

Section: Related Workmentioning

confidence: 99%

Keyphrase Extraction of News Web Pages

Arya¹,

Dwivedi²

2018

IJEME

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Keyphrase extraction from news web pages is an important task for news documents retrieval and summarization. Keyphrases are like index terms that enclose the important information about document content. Keyphrases actually offer concise and precise description of document content. Key phrases are considered as a single word or a combination of more than one word that represent the important concepts in a text documents. The aim of this paper is to develop and evaluate an automatic keyphrases extraction approach for news web pages. Our approach identifies the candidate keyphrases from documents and chooses those candidate keyphrase having highest weight score. Weight formula combines the feature set that includes TF*IDF, phrase disatnce in documents and lexical chain that is based on WordNet to represent semantic relations between words. The experimental results show that the performance of our approach is better than the contemporary approaches today.

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Section: Related Workmentioning

confidence: 99%

Keyphrase Extraction of News Web Pages

Arya¹,

Dwivedi²

2018

IJEME

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“…For example, authors in [20] [19] exploit traditional term frequency, inverse document frequency, and position (binary) features. In another approach, authors provided a cluster-based model in order to highlight parts of the text that are semantically related [21].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Approach for Keyphrase Extraction from English Document

Emu¹,

Ahmed²,

Islam³

et al. 2017

IJISA

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“…Machine learning algorithm is trained to give the most appropriate model parameters and finally applied to the keyword extraction testing data set. Such as based on SVM model [6] method based on PageRank algorithm [7] and neural network [8].…”

Section: Related Workmentioning

confidence: 99%

Keyword extraction of web pages based on domain thesaurus

Wang

Zhang

et al. 2014

2014 IEEE 3rd International Conference on Cloud Computing and Intelligence Systems

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This paper presents a keyword extraction method of web pages based on domain thesaurus. The method extracts keywords from web pages based on traditional statistic features, such as frequency and location, and it also evaluates the weight of candidate keywords combining with their relation of domain thesaurus. This method can effectively identify domain keywords of web pages with low frequency but more information in specific area. Based on the web pages keywords extraction of environment domain as an example, this paper introduces the framework and algorithm of the method. Experimental results show that, compared with the traditional TF -IDF method, this method has a better keyword extraction performance in environment-related web pages, an average of 20% recall rate, and an average of 15 percent accuracy rate.

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Automatic Keyphrases Extraction from Document Using Neural Network

Cited by 29 publications

References 18 publications

Keyphrase Extraction of News Web Pages

Keyphrase Extraction of News Web Pages

An Efficient Approach for Keyphrase Extraction from English Document

Keyword extraction of web pages based on domain thesaurus

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