Scalable clustering methods for the name disambiguation problem

On, Byung-Won; Lee, Ingyu; Lee, Dongwon

doi:10.1007/s10115-011-0397-1

Cited by 17 publications

(8 citation statements)

References 32 publications

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“…Two multi-level scalable algorithms for AND are proposed by On et al . (2012). First is a multi-level graph partitioning (MGP) algorithm, and the second is a multi-level graph partitioning and merging (MGPM) algorithm.…”

Section: Selective Author Name Disambiguation Techniquesmentioning

confidence: 99%

A survey of author name disambiguation techniques: 2010–2016

Hussain

Asghar

2017

The Knowledge Engineering Review

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Digital libraries content and quality of services are badly affected by the author name ambiguity problem in the citations and it is considered as one of the hardest problems faced by the digital library researchers. Several techniques have been proposed in the literature for the author name ambiguity problem. In this paper, we reviewed some recently presented author name disambiguation techniques and give some challenges and future research directions. We analyze the recent advancements in this field and classify these techniques into supervised, unsupervised, semi-supervised, graph-based and heuristic-based techniques according to their problem formulation that is mainly used for the author name disambiguation. A few surveys have been conducted to review different techniques for the author name disambiguation. These surveys highlighted only the methodology adopted for author name disambiguation but did not critically review their shortcomings. This survey provides a detailed review of author name disambiguation techniques available in the literature, makes a comparison of these techniques at an abstract level and discusses their limitations.

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Section: Selective Author Name Disambiguation Techniquesmentioning

confidence: 99%

A survey of author name disambiguation techniques: 2010–2016

Hussain

Asghar

2017

The Knowledge Engineering Review

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“…When the number of clusters is known beforehand, the person name disambiguation task can be formulated as a classification problem. For recent work, see [16,18].…”

Section: Related Workmentioning

confidence: 99%

Result Disambiguation in Web People Search

Berendsen

Kovachev

Nastou

et al. 2012

Lecture Notes in Computer Science

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We study the problem of disambiguating the results of a web people search engine: given a query consisting of a person name plus the result pages for this query, find correct referents for all mentions by clustering the pages according to the different people sharing the name. While the problem has been studied extensively, we discover that the increasing availability of results retrieved from social media platforms causes state-of-the-art methods to break down. We analyze the problem and propose a dual strategy where we distinguish between results obtained from social media platforms and those obtained from other sources. In our dual strategy, the two types of documents are disambiguated separately, using different strategies, and their results are then merged. We study several instantiations for the different stages in our proposed strategy and manage to achieve state-of-the-art performance.

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“…In addition, supervised systems need a significant amount of annotated corpora in order to estimate the model parameters. On et al [20] describe a work to disambiguate person names in digital libraries by using clustering methods based on graph partitioning algorithms that was trained with a subset of DBLP records and a set of web pages previously annotated. The cost of annotating data turns into a limitation when we approach a new language, a new domain, or a different set of classes.…”

Section: Related Workmentioning

confidence: 99%

Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining

et al. 2012

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Named Entity Recognition and Classification (NERC) is an important component of applications like Opinion Tracking, Information Extraction, or Question Answering. When these applications require to work in several languages, NERC becomes a bottleneck because its development requires language-specific tools and resources like lists of names or annotated corpora. This paper presents a lightly supervised system that acquires lists of names and linguistic patterns from large raw text collections in western languages and starting with only a few seeds per class selected by a human expert. Experiments have been carried out with English and Spanish news collections and with the Spanish Wikipedia. Evaluation of NE classification on standard datasets shows that NE lists achieve high precision and reveals that contextual patterns increase recall significantly. Therefore, it would be helpful for applications where annotated NERC data are not available such as those that have to deal with several western languages or information from different domains.

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Scalable clustering methods for the name disambiguation problem

Cited by 17 publications

References 32 publications

A survey of author name disambiguation techniques: 2010–2016

A survey of author name disambiguation techniques: 2010–2016

Result Disambiguation in Web People Search

Lightly supervised acquisition of named entities and linguistic patterns for multilingual text mining

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