A New Extraction Algorithm for Hierarchical Keyword Using Text Social Network

Lee, Da-Young; Kim, Kyung-Rim; Cho, Hwan-Gue

doi:10.1007/978-981-10-0557-2_86

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Table 5. Comparing the algorithm with other related algorithms Algoritm Accuracy The Proposed Algorithm 91.3% Graph [27] 80% Kp [28] 47.7% MSF [29] 60% GATE [30] 64.4% Habibi[1] 75% Single-Document [31] 83.2%…”

Section: The Comparison Of the Obtained Results With The Other Relatementioning

confidence: 99%

Improving keyword extraction in multilingual texts

Hashemzahde

Abdolrazzagh-Nezhad²

2020

IJECE

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The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to that language holds a high rank based on the keywords criteria in other languages, as well. To achieve to this aim, the average TF-IDF of the candidate words were calculated for the same and the other languages. Then the words with the higher averages TF-IDF were chosen as the extracted keywords. The obtained results indicat that the algorithms’ accuracis of the multilingual texts in term frequency-inverse document frequency (TF-IDF) algorithm, graph-based algorithm, and the improved proposed algorithm are 80%, 60.65%, and 91.3%, respectively.

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Section: The Comparison Of the Obtained Results With The Other Relatementioning

confidence: 99%

Improving keyword extraction in multilingual texts

Hashemzahde

Abdolrazzagh-Nezhad²

2020

IJECE

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show abstract

“…Searching and navigation among this huge volume of documents is very difficult, time-consuming and confusing, and it's almost impossible to find the intended materials without using text-mining techniques. Automatic extraction of keywords is a subset of text mining that facilitates the organization and retrieval of text data and helps the user understand and access information in a text (document) in a short time [1]. Keywords are a set of words (a word or set of words) is a text (document) that can be an indicator of its contents [2].…”

Section: Introductionmentioning

confidence: 99%

PAKE: a supervised approach for Persian automatic keyword extraction using statistical features

2019

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Keywords are a collection of important words in a document that are the core topic of the discussion. This paper proposes a hybrid method for automatically extracting keywords from Persian documents and web pages. In the proposed method, firstly, based on linguistic knowledge, processing was performed at word and letter levels to optimize of the analysis. Then a new statistical features set is defined and extracted at the word level. At the final stage, keywords are determined using the SVM algorithm. Also, in this paper, due to the lack of a corpus for evaluating the methods of automatic extraction of Persian keywords, a large-scale corpus has been developed and introduced. The achieved F-measure for keywords and non-keywords are 99.89% and 99.99% respectively.

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A New Extraction Algorithm for Hierarchical Keyword Using Text Social Network

Cited by 2 publications

References 8 publications

Improving keyword extraction in multilingual texts

Improving keyword extraction in multilingual texts

PAKE: a supervised approach for Persian automatic keyword extraction using statistical features

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