Keyphrase Extraction Based on Prior Knowledge

He, Guoxiu; Fang, Juemin; Cui, Haoran; Wu, Chuan; Lu, Wei

doi:10.1145/3197026.3203869

Cited by 6 publications

(8 citation statements)

References 3 publications

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“…Bag of words (BoWs) is one of the most commonly used representations for text classification, an example being keyphrase extraction ( Caragea et al, 2016 ; He et al, 2018 ). BoW represents text as a set of unordered word-level tokens, without considering syntactical and sequential information.…”

Section: Related Workmentioning

confidence: 99%

Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks

Kandimalla

Rohatgi

et al. 2021

Front. Res. Metr. Anal.

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Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using nine million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro-F1 measure of 0.76 with F1 of individual subject categories ranging from 0.50 to 0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers.

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Section: Related Workmentioning

confidence: 99%

Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks

Kandimalla

Rohatgi

et al. 2021

Front. Res. Metr. Anal.

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“…We randomly select up to 150K abstracts per SC. This upper limit is based on our preliminary study (Wu et al, 2018). The ratio between the training and testing corpus is 9:1.…”

Section: Methodsmentioning

confidence: 99%

“…The majority of words and phrases included in the vocabulary extracted from these articles provides general descriptions of knowledge, which are significantly different from those used in scholarly articles which describe specific domain knowledge. Statistically, the overlap between the vocabulary of pretrained GloVe (6 billion tokens) and WoS is only 37% (Wu et al, 2018). Nearly all of the WE models can be retrained.…”

Section: Retrained We Modelsmentioning

confidence: 99%

“…In this work, we pose the SC problem as one of multiclass classification in which one SC is assigned to each paper. In a preliminary study, we investigated feature-based machine learning methods to classify research papers into 6 SCs (Wu et al, 2018). Here, we extend that study and propose a system that classifies scholarly papers into 104 SCs using only a paper's abstract.…”

Section: Introductionmentioning

confidence: 99%

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Large Scale Subject Category Classification of Scholarly Papers with Deep Attentive Neural Networks

Kandimalla¹,

Rohatgi²,

Wu³

et al. 2020

Preprint

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Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category information can be used for building faceted search for digital library search engines. This can significantly assist users in narrowing down their search space of relevant documents. Unfortunately, many academic papers do not have such information as part of their metadata. Existing methods for solving this task usually focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few or no citations cannot be classified using such methods. Here, we propose a deep attentive neural network (DANN) that classifies scholarly papers using only their abstracts. The network is trained using 9 million abstracts from Web of Science (WoS). We also use the WoS schema that covers 104 subject categories. The proposed network consists of two bi-directional recurrent neural networks followed by an attention layer. We compare our model against baselines by varying the architecture and text representation. Our best model achieves micro-F 1 measure of 0.76 with F 1 of individual subject categories ranging from 0.50-0.95. The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism. The combination of word vectors with TFIDF outperforms character and sentence level embedding models. We discuss imbalanced samples and overlapping categories and suggest possible strategies for mitigation. We also determine the subject category distribution in CiteSeerX by classifying a random sample of one million academic papers.

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“…It captures the variation in terms and provides a consistent method to assign preferred terms for similar concepts [33]. Previous literature shows that integrating a controlled vocabulary into the extraction process of words and phrases can enhance the results [34][35][36]. We performed this phase using python scripts and delivered the data that were cleaned up in the csv file format.…”

Section: Data Pre-processingmentioning

confidence: 99%

Theme Mapping and Bibliometrics Analysis of One Decade of Big Data Research in the Scopus Database

Parlina

Ramli²,

Murfi³

2020

Information

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Recently, the popularity of big data as a research field has shown continuous and wide-scale growth. This study aims to capture the scientific structure and topic evolution of big data research using bibliometrics and text mining-based analysis methods. Bibliographic data of journal articles regarding big data published between 2009 to 2018 were collected from the Scopus database and analyzed. The results show a significant growth of publications since 2014. Furthermore, the findings of this study highlight the core journals, most cited articles, top productive authors, countries, and institutions. Secondly, a unique approach to identifying and analyzing major research themes in big data publications was proposed. Keywords were clustered, and each cluster was labeled as a theme. Moreover, the papers were divided into four sub-periods to observe the thematic evolution. The theme mapping reveals that research on big data is dominated by big data analytics, which covers methods, tools, supporting infrastructure, and applications. Other critical aspects of big data research are security and privacy. Social networks and the Internet of things are significant sources of big data, and the resources and services offered by cloud computing strongly support the management and processing of big data.

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Keyphrase Extraction Based on Prior Knowledge

Cited by 6 publications

References 3 publications

Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks

Large Scale Subject Category Classification of Scholarly Papers With Deep Attentive Neural Networks

Large Scale Subject Category Classification of Scholarly Papers with Deep Attentive Neural Networks

Theme Mapping and Bibliometrics Analysis of One Decade of Big Data Research in the Scopus Database

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