Name2Vec: Personal Names Embeddings

Foxcroft, Jérémy; d’Alessandro, Adrian; Antonie, Luiza

doi:10.1007/978-3-030-18305-9_52

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…Since these problems are known for decades, several studies [23,19,8,15,6,26,35,16,17] have been conducted using different machine learning approaches. This problem is often tackled using supervised approaches such as Support Vector Machine (SVM) [10], Bayesian Classification [35] and Neural networks [30].…”

Section: Introductionmentioning

confidence: 99%

Whois? Deep Author Name Disambiguation using Bibliographic Data

Boukhers¹,

Bahubali²

2022

Preprint

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As the number of authors is increasing exponentially over years, the number of authors sharing the same names is increasing proportionally. This makes it challenging to assign newly published papers to their adequate authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use a collection from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, which is represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.

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Section: Introductionmentioning

confidence: 99%

Whois? Deep Author Name Disambiguation using Bibliographic Data

Boukhers¹,

Bahubali²

2022

Preprint

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“…Since these problems are known for decades, several studies [1][2][3][4][5][6][7][8][9] have been conducted using different machine learning approaches. This problem is often tackled using supervised approaches such as Support Vector Machine (SVM) [10], Bayesian Classification [7] and Neural networks (NN) [11].…”

Section: Introductionmentioning

confidence: 99%

“…It is estimated that about 114 million people share 300 common names 2. In the DBLP database, there are 27 exact matches of 'Chen Li', 23 reverse matches and more than 1000 partial matches3 Xu, Zhihao, et al "Teleoperating a formation of car-like rovers under time delays." Proceedings of the 30th Chinese Control Conference.…”

mentioning

confidence: 99%

Whois? Deep Author Name Disambiguation Using Bibliographic Data

Boukhers

Bahubali

2022

Lecture Notes in Computer Science

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In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it challenging to assign newly published papers to their respective authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.

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“…To overcome the above mentioned problems, several approaches have been proposed and are integrated in different bibliographic systems [7, 9, 15-18, 21, 26, 28]. Most of these approaches rely on the author name string [9]. Other approaches consider the strings of the co-authors and the titles along with the author name strings [21].…”

Section: Introductionmentioning

confidence: 99%

Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data

Boukhers¹,

Nagaraj²,

Thulsi³

et al. 2021

Preprint

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Author name ambiguity remains a critical open problem in digital libraries due to synonymy and homonymy of names. In this paper, we propose a novel approach to link author names to their real-world entities by relying on their co-authorship pattern and area of research. Our supervised deep learning model identifies an author by capturing his/her relationship with his/her co-authors and area of research, which is represented by the titles and sources of the target author's publications. These attributes are encoded by their semantic and symbolic representations. To this end, Bib2Auth uses ∼ 22K bibliographic records from DBLP repository and is trained with each pair of co-authors. The extensive experiments have proved the capability of the approach to distinguish between authors sharing the same name and recognize authors with different name variations. Bib2Auth has shown good performance on a relatively large dataset, which qualifies it to be directly integrated into bibliographic indices.

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Name2Vec: Personal Names Embeddings

Cited by 8 publications

References 6 publications

Whois? Deep Author Name Disambiguation using Bibliographic Data

Whois? Deep Author Name Disambiguation using Bibliographic Data

Whois? Deep Author Name Disambiguation Using Bibliographic Data

Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data

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