Metadata Enrichment via Topic Models for Author Name Disambiguation

Bernardi, Raffaella; Le, Dieu-Thu

doi:10.1007/978-3-642-23160-5_7

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…There are also various applications strongly related to author name disambiguation, such as word sense disambiguation, topic modeling, research assessment, collaboration networks, reference analysis, and stylometry, as recently reviewed (Smalheiser & Torvik, ; Elliot, ; Ferreira, Gonçalves, & Laender, ). These applications can benefit the name resolution task by providing important information other than conventional features extracted from metadata (Lin & Wilbur, ; Yin, Han, & Yu, ; Varol, Stafford, Kovvuri, & Chitti, ; Bernardi & Le, ). On the other hand, high‐quality name disambiguation can also benefit these applications.…”

Section: Introductionmentioning

confidence: 99%

“…Some systems combine various citation features (Han, Zha, & Giles, ; Torvik, Weeber, Swanson, & Smalheiser, ; Soler, ; Yin et al., ), and some combine citation features with disambiguating heuristics based on predefined patterns (Cota, Ferreira, Nascimento, Gonçalves, & Laender, ). Besides conventional citation information, some works (Song et al., ; Yang, Peng, Jiang, Lee, & Ho, ; Bernardi & Le, ) also exploit topic models to obtain features. McRae‐Spencer and Shadbolt () include self‐citation information as features and Levin et al.…”

Section: Introductionmentioning

confidence: 99%

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Author name disambiguation forPubMed

Liu

Doğan

Kim

et al. 2013

Asso for Info Science & Tech

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Log analysis shows that PubMed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity may lead to irrelevant retrieval results. To improve the PubMed user experience with author name queries, we designed an author name disambiguation system consisting of similarity estimation and agglomerative clustering. A machine-learning method was employed to score the features for disambiguating a pair of papers with ambiguous names. These features enable the computation of pairwise similarity scores to estimate the probability of a pair of papers belonging to the same author, which drives an agglomerative clustering algorithm regulated by 2 factors: name compatibility and probability level. With transitivity violation correction, high precision author clustering is achieved by focusing on minimizing false-positive pairing. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results. When compared with a state-of-the-art system, our evaluation shows that among all the pairs the lumping error rate drops from 10.1% to 2.2% for our system, while the splitting error rises from 1.8% to 7.7%. This results in an overall error rate of 9.9%, compared with 11.9% for the state-of-the-art method. Other evaluations based on gold standard data also show the increase in accuracy of our clustering. We attribute the performance improvement to the machine-learning method driven by a large-scale training set and the clustering algorithm regulated by a name compatibility scheme preferring precision. With integration of the author name disambiguation system into the PubMed search engine, the overall click-through-rate of PubMed users on author name query results improved from 34.9% to 36.9%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Author name disambiguation forPubMed

Liu

Doğan

Kim

et al. 2013

Asso for Info Science & Tech

View full text Add to dashboard Cite

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Author gender metadata augmentation of hathitrust digital library

Peng

Chen

Plale

2014

Proc of Assoc for Info

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Bibliographic metadata is essential for digital library resource description. Especially as the size and number of bibliographic entities grows, high‐quality metadata enables richer forms of digital library access, search, and use. Metadata records can be enriched through automated techniques. For example, a digital humanities scholar might use the gender of a set of authors during their literature analysis. In this study, we undertook to enrich the metadata description of a large‐scale digital library, the HathiTrust (HT) digital library, specifically by determining the gender of authors of the public domain portion of the collection. The results are stored to a separate Solr index accessible through the HathiTrust Research Center services. This study, which successfully resolved in 78.9% of the cases the gender of authors in the HT public domain corpus, suggests future research directions in capturing and representing the provenance of the contributing sources to enhance trust, and in machine learning to resolve the remaining names.

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Metadata Enrichment via Topic Models for Author Name Disambiguation

Cited by 2 publications

References 8 publications

Author name disambiguation forPubMed

Author name disambiguation forPubMed

Author gender metadata augmentation of hathitrust digital library

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