scholarly journals CONNA: Addressing Name Disambiguation on The Fly

2020 ◽  
pp. 1-1
Author(s):  
Bo Chen ◽  
Jing Zhang ◽  
Jie Tang ◽  
Lingfan Cai ◽  
Zhaoyu Wang ◽  
...  
Keyword(s):  
Name Disambiguation

Name disambiguation based on query expansion

2013 ◽  
Vol 32 (9) ◽  
pp. 2488-2490
Author(s):  
Xin-xin YANG ◽  
Pei-feng LI ◽  
Qiao-ming ZHU
Keyword(s):  
Query Expansion ◽  
Name Disambiguation

scholarly journals AutoSense Model for Word Sense Induction

2019 ◽  
Vol 33 ◽  
pp. 6212-6219 ◽  
Author(s):  
Reinald Kim Amplayo ◽  
Seung-won Hwang ◽  
Min Song
Keyword(s):  
Latent Variable ◽  
Word Sense ◽  
Name Disambiguation ◽  
Variable Model ◽  
Fine Grained ◽  
Word Sense Induction ◽  
Author Name Disambiguation ◽  
Competing Models ◽  
Word Senses ◽  
Better Than

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the stateof-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.

An unsupervised heuristic-based hierarchical method for name disambiguation in bibliographic citations

2010 ◽  
Vol 61 (9) ◽  
pp. 1853-1870 ◽  
Author(s):  
Ricardo G. Cota ◽  
Anderson A. Ferreira ◽  
Cristiano Nascimento ◽  
Marcos André Gonçalves ◽  
Alberto H. F. Laender
Keyword(s):  
Name Disambiguation ◽  
Hierarchical Method

Effect of Chinese characters on machine learning for Chinese author name disambiguation: A counterfactual evaluation

2021 ◽  
pp. 016555152110181
Author(s):  
Jinseok Kim ◽  
Jenna Kim ◽  
Jinmo Kim
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Digital Libraries ◽  
Chinese Characters ◽  
Name Disambiguation ◽  
Authority Control ◽  
Author Name Disambiguation ◽  
Bibliographic Data ◽  
Chinese Author

Chinese author names are known to be more difficult to disambiguate than other ethnic names because they tend to share surnames and forenames, thus creating many homonyms. In this study, we demonstrate how using Chinese characters can affect machine learning for author name disambiguation. For analysis, 15K author names recorded in Chinese are transliterated into English and simplified by initialising their forenames to create counterfactual scenarios, reflecting real-world indexing practices in which Chinese characters are usually unavailable. The results show that Chinese author names that are highly ambiguous in English or with initialised forenames tend to become less confusing if their Chinese characters are included in the processing. Our findings indicate that recording Chinese author names in native script can help researchers and digital libraries enhance authority control of Chinese author names that continue to increase in size in bibliographic data.

scholarly journals GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Chih-Hsuan Wei ◽  
Hung-Yu Kao ◽  
Zhiyong Lu
Keyword(s):  
Text Mining ◽  
Protein Domains ◽  
Gene Families ◽  
Integrative Approach ◽  
Name Disambiguation ◽  
Gene Mention ◽  
Web Based ◽  
Gene Normalization ◽  
Document Level ◽  
Gene Normalization Task

The automatic recognition of gene names and their associated database identifiers from biomedical text has been widely studied in recent years, as these tasks play an important role in many downstream text-mining applications. Despite significant previous research, only a small number of tools are publicly available and these tools are typically restricted to detecting only mention level gene names or only document level gene identifiers. In this work, we report GNormPlus: an end-to-end and open source system that handles both gene mention and identifier detection. We created a new corpus of 694 PubMed articles to support our development of GNormPlus, containing manual annotations for not only gene names and their identifiers, but also closely related concepts useful for gene name disambiguation, such as gene families and protein domains. GNormPlus integrates several advanced text-mining techniques, including SimConcept for resolving composite gene names. As a result, GNormPlus compares favorably to other state-of-the-art methods when evaluated on two widely used public benchmarking datasets, achieving 86.7% F1-score on the BioCreative II Gene Normalization task dataset and 50.1% F1-score on the BioCreative III Gene Normalization task dataset. The GNormPlus source code and its annotated corpus are freely available, and the results of applying GNormPlus to the entire PubMed are freely accessible through our web-based tool PubTator.

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