TOP-Rank: A TopicalPostionRank for Extraction and Classification of Keyphrases in Text

Awan, Mubashar Nazar; Beg, Mirza Omer

doi:10.1016/j.csl.2020.101116

Cited by 22 publications

(11 citation statements)

References 5 publications

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“…The first two methods aim to abate the feature space dimension, and the third method aims to lower the number of training examples. Awan et al [23] proposed TOP-Rank to extract the most relevant key phrases from text and then perform classification. Pintas et al [5] classified term-frequency-inverse-document-frequency (TF-IDF) as an unsupervised technique in the survey of feature selection.…”

Section: Feature Selection For Text Classificationmentioning

confidence: 99%

Few-Shot Text Classification with Global–Local Feature Information

Wang

Cheng

et al. 2022

Sensors

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Meta-learning frameworks have been proposed to generalize machine learning models for domain adaptation without sufficient label data in computer vision. However, text classification with meta-learning is less investigated. In this paper, we propose SumFS to find global top-ranked sentences by extractive summary and improve the local vocabulary category features. The SumFS consists of three modules: (1) an unsupervised text summarizer that removes redundant information; (2) a weighting generator that associates feature words with attention scores to weight the lexical representations of words; (3) a regular meta-learning framework that trains with limited labeled data using a ridge regression classifier. In addition, a marine news dataset was established with limited label data. The performance of the algorithm was tested on THUCnews, Fudan, and marine news datasets. Experiments show that the SumFS can maintain or even improve accuracy while reducing input features. Moreover, the training time of each epoch is reduced by more than 50%.

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Section: Feature Selection For Text Classificationmentioning

confidence: 99%

Few-Shot Text Classification with Global–Local Feature Information

Wang

Cheng

et al. 2022

Sensors

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“…( 2005 ), Biswas et al. ( 2018 ), Florescu and Caragea ( 2017 ), Awan and Beg ( 2020 ) Semantic connectivity Linking distinct concepts in GoW Campos et al. ( 2020 ) Casing The abbreviation and uppercase representation of a word in GoW Campos et al.…”

Section: Statistical Properties Of Graph Of Wordsmentioning

confidence: 99%

A survey on different dimensions for graphical keyword extraction techniques

Garg

2021

Artif Intell Rev

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“…Yet, they lack the rich and necessary information regarding the data and are very inefficient (Vahidnia, Abbasi, & Abbass, 2020). However, in addition to their usage in text representation (Zhang, Li et al, 2017;, it is also very common to use TF-IDF for keyword extraction, as it has been incorporated in literature for similar purposes (Awan & Beg, 2020;Peganova, Rebrova, & Nedumov, 2019;Radu et al, 2020).…”

Section: Journal Of Data and Information Sciencementioning

confidence: 99%

“…Other methods are more advanced, taking advantage of ranking algorithms (e.g. TOP-Rank (Awan & Beg, 2020)). ComboBasic (Astrakhantsev, 2015).…”

Section: Labeling Of the Clustersmentioning

confidence: 99%

Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

Vahidnia

Abbasi

Abbass

2021

Journal of Data and Information Science

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Purpose Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific fields. This also helps in having a better collaboration with governments and businesses. This study aims to investigate the development of research fields over time, translating it into a topic detection problem. Design/methodology/approach To achieve the objectives, we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic documents. Document embedding approaches are utilized to transform documents into vector-based representations. The proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms (i.e. LDA) against a benchmark dataset. A case study is also conducted exploring the evolution of Artificial Intelligence (AI) detecting the research topics or sub-fields in related AI publications. Findings Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark dataset. Using the proposed method, we also show how the topics have evolved in the period of the recent 30 years, taking advantage of a keyword extraction method for cluster tagging and labeling, demonstrating the context of the topics. Research limitations We noticed that it is not possible to generalize one solution for all downstream tasks. Hence, it is required to fine-tune or optimize the solutions for each task and even datasets. In addition, interpretation of cluster labels can be subjective and vary based on the readers’ opinions. It is also very difficult to evaluate the labeling techniques, rendering the explanation of the clusters further limited. Practical implications As demonstrated in the case study, we show that in a real-world example, how the proposed method would enable the researchers and reviewers of the academic research to detect, summarize, analyze, and visualize research topics from decades of academic documents. This helps the scientific community and all related organizations in fast and effective analysis of the fields, by establishing and explaining the topics. Originality/value In this study, we introduce a modified and tuned deep embedding clustering coupled with Doc2Vec representations for topic extraction. We also use a concept extraction method as a labeling approach in this study. The effectiveness of the method has been evaluated in a case study of AI publications, where we analyze the AI topics during the past three decades.

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TOP-Rank: A TopicalPostionRank for Extraction and Classification of Keyphrases in Text

Cited by 22 publications

References 5 publications

Few-Shot Text Classification with Global–Local Feature Information

Few-Shot Text Classification with Global–Local Feature Information

A survey on different dimensions for graphical keyword extraction techniques

Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

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