Cost-effective on-demand associative author name disambiguation

Lecture Notes in Computer Science, 2009

Name ambiguity in the context of bibliographic citations is one of the hardest problems currently faced by the digital library community. Several methods have been proposed in the literature, but none of them provides the perfect solution for the problem. More importantly, basically all of these methods were tested in limited and restricted scenarios, which raises concerns about their practical applicability. In this work, we deal with these limitations by proposing a synthetic generator of ambiguous authorship records called SyGAR. The generator was validated against a gold standard collection of disambiguated records, and applied to evaluate three disambiguation methods in a relevant scenario.

Synthesis lectures on information concepts, retrieval, and services, 2020

This book deals with a hard problem that is inherent to human language: ambiguity. In particular, we focus on author name ambiguity, a type of ambiguity that exists in digital bibliographic repositories, which occurs when an author publishes works under distinct names or distinct authors publish works under similar names. This problem may be caused by a number of reasons, including the lack of standards and common practices, and the decentralized generation of bibliographic content. As a consequence, the quality of the main services of digital bibliographic repositories such as search, browsing, and recommendation may be severely affected by author name ambiguity. The focal point of the book is on automatic methods, since manual solutions do not scale to the size of the current repositories or the speed in which they are updated. Accordingly, we provide an ample view on the problem of automatic disambiguation of author names, summarizing the results of more than a decade of research on this topic conducted by our group, which were reported in more than a dozen publications that received over 900 citations so far, according to Google Scholar. We start by discussing its motivational issues (Chapter 1). Next, we formally define the author name disambiguation task (Chapter 2) and use this formalization to provide a brief, taxonomically organized, overview of the literature on the topic (Chapter 3). We then organize, summarize and integrate the efforts of our own group on developing solutions for the problem that have historically produced state-of-the-art (by the time of their proposals) results in terms of the quality of the disambiguation results. Thus, Chapter 4 covers HHC -Heuristic-based Clustering, an author name disambiguation method that is based on two specific real-world assumptions regarding scientific authorship. Then, Chapter 5 describes SAND -Self-training Author Name Disambiguator and Chapter 6 presents two incremental author name disambiguation methods, namely INDi -Incremental Unsupervised Name Disambiguation and INC-Incremental Nearest Cluster. Finally, Chapter 7 provides an overview of recent author name disambiguation methods that address new specific approaches such as graph-based representations, alternative predefined similarity functions, visualization facilities and approaches based on artificial neural networks. The chapters are followed by three appendices that cover, respectively: (i) a pattern matching function for comparing proper names and used by some of the methods addressed in this book; (ii) a tool for generating synthetic collections of citation records for distinct experimental tasks; and (iii) a number of datasets commonly used to evaluate author name disambiguation methods. In summary, the book organizes a large body of knowledge and work in the area of author name disambiguation in the last decade, hoping to consolidate a solid basis for future developments in the field.

K. Jokinen, D. Heylen, …, 2000

In this paper we report preliminary results from an ongoing study that investigates the performance of machine learning classifiers on a diverse set of Natural Language Processing (NLP) tasks. First, we compare a number of popular existing learning methods (Neural networks, Memory-based learning, Rule induction, Decision trees, Maximum Entropy, Winnow Perceptrons, Naive Bayes and Support Vector Machines), and discuss their properties visà vis typical NLP data sets. Next, we turn to methods to optimize the parameters of single learning methods through cross-validation and evolutionary algorithms. Then we investigate how we can get the best of all single methods through combination of the tested systems in classifier ensembles. Finally we discuss new and more thorough methods of automatically constructing ensembles of classifiers based on the techniques used for parameter optimization.

Lecture Notes in Computer Science, 2022

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.

IEEE Access

Author Name Disambiguation (AND) has emerged as a significant challenge in the bibliometric context with the growing volume of scientific literature. When citations written by different authors have the same names (polysemy or homonym names), and when an author has different names, there is ambiguity (synonyms or name variants). It is difficult to associate a citation with the correct author. Polysemy and synonyms cause merging and splitting anomalies in the citations. These anomalies affect the quantification of an author's productivity (bibliometric analysis) and the reliability and quality of the information retrieved. Many techniques for AND have been proposed in the literature; most of them do not go beyond string matching or text matching. Most do not consider the context or semantics of the terms used in the citations. The AND problem is resolved semantically in this paper using the deep learning technique on the PubMed dataset. The experimental results show that the proposed method achieves overall (11.72%, 12.5%, and 12.1%) higher precision, recall, and f-measure than the pairwise class classification.

2000

In this paper we report preliminary results from an ongoing study that investigates the performance of machine learning classifiers on a diverse set of Natural Language Processing (NLP) tasks. First, we compare a number of popular existing learning methods (Neural networks, Memory-based learning, Rule induction, Decision trees, Maximum Entropy, Winnow Perceptrons, Naive Bayes and Support Vector Machines), and discuss their properties vis à vis typical NLP data sets. Next, we turn to methods to optimize the parameters of single learning methods through cross-validation and evolutionary algorithms. Then we investigate how we can get the best of all single methods through combination of the tested systems in classifier ensembles. Finally we discuss new and more thorough methods of automatically constructing ensembles of classifiers based on the techniques used for parameter optimization.

2012

Author name ambiguity in the context of bibliographic citations is a very hard problem. It occurs when there are citation records of a same author under distinct names or when there exists citation records belonging to distinct authors with very similar names. Among the several methods proposed in the literature, the most effective ones are those that perform a direct assignment of the records to their respective authors by means of the application of supervised machine learning techniques. However, those methods usually need large amounts of labeled training examples to properly disambiguate the author names. To deal with this issue, in previous work, we have proposed a method that automatically obtains and labels the training examples, showing competitive performance compared to representative author name disambiguation methods. In this work, we propose to improve our previous method by exploiting user relevance feedback. In more details we select a very small portion of the citation records for which our method was mostly unsure about the correct authorship and ask the administrators for labeling them. This feedback is then used to improve the effectiveness of the whole process. In our experimental evaluation, we observed that with a very small labeling effort (usually around 5% of the records), the overall disambiguation effectiveness improves by almost 10% on average, with gains of up to 61% in some of the largest ambiguous groups.

2012

Author name disambiguation allows to distinguish between two or more authors sharing the same name. In a previous paper, we have proposed a name disambiguation framework in which for each author name in each article we build a context consisting of classification codes, bibliographic references, co-authors, etc. Then, by pair wise comparison of contexts, we have been grouping contributions likely referring to the same people. In this paper we examine which elements of the context are most effective in author name disambiguation. We employ linear Support Vector Machines (SVM) to find the most influential features.

Author name ambiguity is one of the problems that decrease the quality and reliability of information retrieved from digital libraries. Existing methods have tried to solve this problem by predefining a feature set based on expert's knowledge for a specific dataset. In this paper, we propose a new approach which uses deep neural network to learn features automatically for solving author name ambiguity. Additionally, we propose the general system architecture for author name disambiguation on any dataset. We evaluate the proposed method on a dataset containing Vietnamese author names. The results show that this method significantly outperforms other methods that use predefined feature set. The proposed method achieves 99.31% in terms of accuracy. Prediction error rate decreases from 1.83% to 0.69%, i.e., it decreases by 1.14%, or 62.3% relatively compared with other methods that use predefined feature set .

International Journal on Digital Libraries

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it is challenging to assign newly published papers to their respective authors. Therefore, author name ambiguity is considered a critical open problem in digital libraries. This paper proposes an author name disambiguation 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 r...