sCAKE: Semantic Connectivity Aware Keyword Extraction

Duari, Swagata; Bhatnagar, Vasudha

doi:10.1016/j.ins.2018.10.034

Cited by 55 publications

(31 citation statements)

References 32 publications

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“…Hulth2003 and Krapivin2009 have been widely adopted for evaluating the performance of keyword extraction algorithms, e.g., [45][46][47][48] use Hulth2003 as a test sample for keyword extraction, and Krapivin2009 is used by [46][47][48]. Both of them are representative datasets for keyword extraction, which contain papers with different topics and the abstracts of the papers have kinds of text lengths.…”

Section: Experimental Settings a Datasetsmentioning

confidence: 99%

An Empirical Study of TextRank for Keyword Extraction

Zhang

Yue

et al. 2020

IEEE Access

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As a typical keyword extraction technology, TextRank has been used in a wide variety of commercial applications, including text classification, information retrieval and clustering. In these applications, the parameters of TextRank, including the co-occurrence window size, iteration number and decay factor, are set roughly, which might affect the effectiveness of returned results. In this work, we conduct an empirical study on TextRank, towards finding optimal parameter settings for keyword extraction. The experiments are done in Hulth2003 and Krapivin2009 datasets, which are two real datasets. We first remove the stop word by an open published English stop word list XPO6. And then, we extract the word stems by Porter Stemmer. Porter Stemmer is a tool which can find the stems of words with multiple variants, discard redundant information, strengthen the filtering effect, and extract the effective features of the text fully. We carry out extensive experiments to evaluate the effects of the parameters to keywords extraction, and evaluate the effectiveness of corresponding results by Precision, Recall and Accuracy. Experimental results show that TextRank shows the best performance when setting co-occurrence window size w = 3, iteration number t = 20, decay factor c = 0.9 and rank k = 10 respectively, and the results are independent of the text length.

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Section: Experimental Settings a Datasetsmentioning

confidence: 99%

An Empirical Study of TextRank for Keyword Extraction

Zhang

Yue

et al. 2020

IEEE Access

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“…[16] introduced a SemGraph approach to extract keywords from a collection of texts through building semantic relationship graphs based on WordNet, which can select the words with statistical significance. [29] proposed a parameterless method for constructing graph of text that captures the contextual relationship between words, and designed a novel word scoring method that aims to capture contextual hierarchy, semantic connectivity and position weight of words.…”

Section: Related Workmentioning

confidence: 99%

Automatic Keywords Extraction Based on Co-Occurrence and Semantic Relationships Between Words

Mao¹,

Huang²,

Li³

et al. 2020

IEEE Access

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Automatic keywords extraction is a method that extracts words or phrases from a document which can express the main idea of the document. In this paper, we propose an unsupervised keywords extraction framework for individual documents, which improves the keywords extraction from two aspects. In the step of candidate keywords selection, we use the methods of removing the stopwords, regular matching, and length filtering to reduce the number of candidate keywords, but improve the quality. In the step of scoring words, we use word co-occurrence, semantic relationships (WordNet, Word Embedding, Normalized Google Distance), and three ways to combine word co-occurrence and semantic relationships to measure the weight of edges in the graph model. In experiments, we use Precision, Recall, and F1-measure values as evaluation criteria to compare all keywords extraction methods we proposed with other strong baseline methods in two datasets. According to the results of experiments, methods under our proposed framework achieve good results. We verify that the methods of using both word co-occurrence and semantic relationships have a better effect on keywords extraction than using co-occurrence or semantic relationships only. At the same time, we also find that for the keywords extraction of individual documents, the method of using co-occurrence between words has a better effect than semantic relationships.

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“…Graph-based methods are also among the unsupervised methods [22]. The works of [22][23][24] are examples of graph-based methods for word extraction. In unsupervised methods, there is no need for training data and the most important contextual phrases could be extracted by using the ranking strategies.…”

Section: Literature Reviewmentioning

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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sCAKE: Semantic Connectivity Aware Keyword Extraction

Cited by 55 publications

References 32 publications

An Empirical Study of TextRank for Keyword Extraction

An Empirical Study of TextRank for Keyword Extraction

Automatic Keywords Extraction Based on Co-Occurrence and Semantic Relationships Between Words

Improving keyword extraction in multilingual texts

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