Keyphrase Cloud Generation of Broadcast News

Marujo, Luís; Viveiros, Márcio Eduardo de Melo; Neto, João Paulo

doi:10.48550/arxiv.1306.4606

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…This dataset contains 500 English-language broadcast news items, each with 50 documents, from ten distinct groups (crime, art and culture, business, fashion, health, politics, world politics, science, technology, as well as sports). It also contains the present keyphrase of 22,345 and the absent keyphrase of 2265, as well as the processing time of 0.203 s [43,48].…”

Section: Corpus Detailsmentioning

confidence: 99%

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

et al. 2022

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Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11%.

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Section: Corpus Detailsmentioning

confidence: 99%

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

et al. 2022

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“…All the details to reproduce our experiments are available at https://github.com/asahi417/kex 6 All the datasets were fetched from a public data repository for keyword extraction data: https://github.com/ LIAAD/KeywordExtractor-Datasets: KPCrowd(Marujo et al, 2013), Inspec(Hulth, 2003), Krapivin2009(Krapivin et al, 2009), SemEval2017, kdd (Gollapalli and Caragea, 2014), www(Gollapalli and Caragea, 2014), wiki20(Medelyan and Witten, 2008), PubMed(Schutz et al, 2008), Schutz2008(Schutz et al, 2008), citeulike180(Medelyan et al, 2009), fao30 and fao780(Medelyan and Witten, 2008), guyen2007(Nguyen and Kan, 2007), and SemEval2010 (Kim et al, 2010.…”

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confidence: 99%

Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction

Ushio¹,

Liberatore²,

Camacho-Collados³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. In particular, term weighting is the basis for keyword extraction. However, there are relatively few evaluation studies that shed light about the strengths and shortcomings of each weighting scheme. In fact, in most cases researchers and practitioners resort to the wellknown tf-idf as default, despite the existence of other suitable alternatives, including graphbased models. In this paper, we perform an exhaustive and large-scale empirical comparison of both statistical and graph-based term weighting methods in the context of keyword extraction. Our analysis reveals some interesting findings such as the advantages of the lessknown lexical specificity with respect to tf-idf, or the qualitative differences between statistical and graph-based methods. Finally, based on our findings we discuss and devise some suggestions for practitioners. 1

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Keyphrase Cloud Generation of Broadcast News

Cited by 2 publications

References 14 publications

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

Back to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction

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