Research on keyword extraction based on Word2Vec weighted TextRank

Wen, Yujun; Yuan, Hui; Zhang, Pengzhou

doi:10.1109/compcomm.2016.7925072

Cited by 10 publications

(4 citation statements)

References 1 publication

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“…In 2019, Li et al [42], they proposed a model where the performance for it was still good and mostly stable with respect to the F-measure, and from the curve of this measure, when the number of extracted keywords N was 7, the F-measure reached a maximum of 43.1% compared to Xia's work. [43], in which, the basic idea of TextRank used for keyword extraction was introduced. The process of constructing candidate keywords and the F-measure up to 37.28%, and all these previous results were less than our results, where F-measures reached 76.75%.…”

Section: Discussionmentioning

confidence: 99%

A Rule-Based Approach to Embedding Techniques for Text Document Classification

Aubaid

Mishra

2020

Applied Sciences

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With the growth of online information and sudden expansion in the number of electronic documents provided on websites and in electronic libraries, there is difficulty in categorizing text documents. Therefore, a rule-based approach is a solution to this problem; the purpose of this study is to classify documents by using a rule-based. This paper deals with the rule-based approach with the embedding technique for a document to vector (doc2vec) files. An experiment was performed on two data sets Reuters-21578 and the 20 Newsgroups to classify the top ten categories of these data sets by using a document to vector rule-based (D2vecRule). Finally, this method provided us a good classification result according to the F-measures and implementation time metrics. In conclusion, it was observed that our algorithm document to vector rule-based (D2vecRule) was good when compared with other algorithms such as JRip, One R, and ZeroR applied to the same Reuters-21578 dataset.

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Section: Discussionmentioning

confidence: 99%

A Rule-Based Approach to Embedding Techniques for Text Document Classification

Aubaid

Mishra

2020

Applied Sciences

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“…The text-based semantic model we adopted as a control is word2vec (Mikolov, Chen, et al, 2013), an efficient word representation algorithm which has gathered vast success in computational linguistics (e.g., Baroni et al, 2014;Wen et al, 2016;Xiao et al, 2018) and is also widely employed in cognitive science (see for instance Hollenstein et al, 2019;Mandera et al, 2017;Mitchell et al, 2008). In particular, we employed the word embeddings generated by the Skip-Gram with Negative Sampling (SGNS) model, which is trained on large corpora of natural language data to predict context words within a certain window based on the target item.…”

Section: Semanticscape Modelmentioning

confidence: 99%

SemanticScape: A Distributional Model of Concepts Grounded in Distance Patterns between Objects

de Varda,

Petilli,

Marelli

2023

Preprint

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Data-driven models of concepts are gaining popularity in Psychology and Cognitive Science. Distributional semantic models represent word meanings as abstract word co-occurrence patterns, and excel at capturing human meaning intuitions about conceptual relationships; however, they lack the explicit links to the physical world that humans acquire through perception. Computer vision neural networks, on the other hand, can produce representations of visually-grounded concepts, but they do not support the extraction of information about the relationships between objects. To bridge the gap between distributional semantic models and computer vision networks, we introduce SemanticScape, a model of semantic concepts grounded in the visual relationships between objects in natural images. The model captures the latent statistics of the spatial organization of objects in the visual environment. Its implementation is based on the calculation of the summed Euclidean distances between all object pairs in visual scenes, which are then abstracted by means of dimensionality reduction. We validate our model against human explicit intuitions on semantic and visual similarity, relatedness, analogical reasoning, and several semantic and visual implicit processing measurements. Our results show that SemanticScape explains variance in human responses in the semantic tasks above and beyond what can be accounted for by standard distributional semantic models and convolutional neural networks; however, it is not predictive of human performance in implicit perceptual tasks.

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“…Keywords are effective terms for bibliometric analysis that investigate the knowledge structure of scientific fields, but are less exhaustive in representing the content of an article [71]. Therefore, the behavior of these terms with time needs to be analyzed.…”

Section: Figure 11 Word Treemapmentioning

confidence: 99%

The Evolution of Business Process Management: A Bibliometric Analysis

2021

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This paper will present the research results for the analysis of the presence and evolution of the term Business Process Management (BPM) in the period 2000-2020 using a literature review with bibliometric analysis. This research sought to evaluate the quantity and quality of empirical support for the use of this tool in organizations. This allowed the researchers to acknowledge and confirm this discipline as an important investigation domain with great potential for helping companies achieve strategic alignment between business and information and communication technologies in the future. The Science Mapping Workflow methodology was used with database and search criteria applied to a total of 1,706 articles related to the subject, which resulted in a total of 624 articles selected for further research. This study identifies the journals that have the most publications about BPM. It concludes that the most promising perspectives are the ones related to Management, Framework and Performance. Even though, from a conceptual viewpoint, performance is the most valued perspective. Lastly, this research is of great interest for academics and professionals who hope to strengthen their knowledge about the BPM concept and find the historical path and the main authors and issues that contribute to knowledge in this scientific field.

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Research on keyword extraction based on Word2Vec weighted TextRank

Cited by 10 publications

References 1 publication

A Rule-Based Approach to Embedding Techniques for Text Document Classification

A Rule-Based Approach to Embedding Techniques for Text Document Classification

SemanticScape: A Distributional Model of Concepts Grounded in Distance Patterns between Objects

The Evolution of Business Process Management: A Bibliometric Analysis

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