Name Disambiguation by Collective Classification

2014 ◽  
pp. 406-417
Author(s):  
Zhongxiang Chen ◽  
Jiafeng Guo ◽  
Yanyan Lan ◽  
Lei Cao ◽  
Xueqi Cheng
Keyword(s):  
Name Disambiguation ◽  
Collective Classification

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

Semi-supervised Collective Classification in Multi-attribute Network Data

2016 ◽  
Vol 45 (1) ◽  
pp. 153-172 ◽  
Author(s):  
Shaokai Wang ◽  
Yunming Ye ◽  
Xutao Li ◽  
Xiaohui Huang ◽  
Raymond Y. K. Lau
Keyword(s):  
Network Data ◽  
Collective Classification

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.

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

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.

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