Hybrid Deep Pairwise Classification for Author Name Disambiguation

Kim, Kunho; Rohatgi, Shaurya; Giles, C. Lee

doi:10.1145/3357384.3358153

Cited by 24 publications

(12 citation statements)

References 12 publications

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“…For the similarity formula, this paper calculates cosine of the angle between the vectors x = ðx 1 , x 2 , ⋯, x n Þ and y = ðy 1 , y 2 , ⋯, y n Þ [13]. It can be written as formula (5). 3 Wireless Communications and Mobile Computing repeatedly assigned to different clusters according to the closest centroid.…”

Section: Framework and Methodologymentioning

confidence: 99%

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Chinese Personal Name Disambiguation Based on Clustering

Fan

Liu

2021

Wireless Communications and Mobile Computing

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Personal name disambiguation is a significant issue in natural language processing, which is the basis for many tasks in automatic information processing. This research explores the Chinese personal name disambiguation based on clustering technique. Preprocessing is applied to transform raw corpus into standardized format at the beginning. And then, Chinese word segmentation, part-of-speech tagging, and named entity recognition are accomplished by lexical analysis. Furthermore, we make an effort to extract features that can better disambiguate Chinese personal names. Some rules for identifying target personal names are created to improve the experimental effect. Additionally, many calculation methods of feature weights are implemented such as bool weight, absolute frequency weight, tf-idf weight, and entropy weight. As for clustering algorithm, an agglomerative hierarchical clustering is selected by comparison with other clustering methods. Finally, a labeling approach is employed to bring forward feature words that can represent each cluster. The experiment achieves a good result for five groups of Chinese personal names.

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Section: Framework and Methodologymentioning

confidence: 99%

“…A feature learning means and an affinity propagation clustering were taken into account. Kim et al [5] combined global features with structure features for author name disambiguation. Global features, extracted from attributes of dataset, formed the textual vector representation.…”

Section: Related Workmentioning

confidence: 99%

Chinese Personal Name Disambiguation Based on Clustering

Fan

Liu

2021

Wireless Communications and Mobile Computing

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“…Several classification models have been used for learning the pairwise similarity function, including Naive Bayes [11], Logistic Regression [12], Support Vector Machines [8,11,13], Decision Trees (C4.5) [2], Random Forests (RF) [6,8,12,14,15], Deep Neural Networks (DNN) [16] and Gradient Boosted Trees (GBT) [6,15,17,18]. Tran et al [16] use DNNs with manually-crafted features, whereas Atarashi et al [19] leveraged a DNN to learn feature representations from bag-of-words vectors.…”

Section: Previous Workmentioning

confidence: 99%

S2AND: A Benchmark and Evaluation System for Author Name Disambiguation

Subramanian¹,

King²,

Downey³

et al. 2021

Preprint

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Author Name Disambiguation (AND) is the task of resolving which author mentions in a bibliographic database refer to the same real-world person, and is a critical ingredient of digital library applications such as search and citation analysis. While many AND algorithms have been proposed, comparing them is difficult because they often employ distinct features and are evaluated on different datasets.In response to this challenge, we present S2AND, a unified benchmark dataset for AND on scholarly papers, as well as an open-source reference model implementation. Our dataset harmonizes eight disparate AND datasets into a uniform format, with a single rich feature set drawn from the Semantic Scholar (S2) database. Our evaluation suite for S2AND reports performance split by facets like publication year and number of papers, allowing researchers to track both global performance and measures of fairness across facet values.Our experiments show that because previous datasets tend to cover idiosyncratic and biased slices of the literature, algorithms trained to perform well on one on them may generalize poorly to others. By contrast, we show how training on a union of datasets in S2AND results in more robust models that perform well even on datasets unseen in training. The resulting AND model also substantially improves over the production algorithm in S2, reducing error by over 50% in terms of B 3 F1. We release our unified dataset, model code, trained models, and evaluation suite to the research community. 1

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“…Others train a model to learn a vector representation by triplets samples without considering any structure information. Kim et al [ 8 ] proposed a hybrid model that makes use of both and train an SVM, a RF, a GBT and a DNN to determine whether a pair of publications is related to the same author or not.…”

Section: Related Workmentioning

confidence: 99%

A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory

2020

Entropy

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Name ambiguity, due to the fact that many people share an identical name, often deteriorates the performance of information integration, document retrieval and web search. In academic data analysis, author name ambiguity usually decreases the analysis performance. To solve this problem, an author name disambiguation task is designed to divide documents related to an author name reference into several parts and each part is associated with a real-life person. Existing methods usually use either attributes of documents or relationships between documents and co-authors. However, methods of feature extraction using attributes cause inflexibility of models while solutions based on relationship graph network ignore the information contained in the features. In this paper, we propose a novel name disambiguation model based on representation learning which incorporates attributes and relationships. Experiments on a public real dataset demonstrate the effectiveness of our model and experimental results demonstrate that our solution is superior to several state-of-the-art graph-based methods. We also increase the interpretability of our method through information theory and show that the analysis could be helpful for model selection and training progress.

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Hybrid Deep Pairwise Classification for Author Name Disambiguation

Cited by 24 publications

References 12 publications

Chinese Personal Name Disambiguation Based on Clustering

Chinese Personal Name Disambiguation Based on Clustering

S2AND: A Benchmark and Evaluation System for Author Name Disambiguation

A Graph-Based Author Name Disambiguation Method and Analysis via Information Theory

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