Difference between revisions of "Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation"
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+ | {{Infobox work | ||
+ | | title = Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation | ||
+ | | date = 2014 | ||
+ | | authors = [[Takuya Makino]] | ||
+ | | doi = 10.3115/v1/E14-4021 | ||
+ | | link = http://aclweb.org/anthology/E/E14/E14-4021.pdf | ||
+ | }} | ||
'''Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Takuya Makino]]. | '''Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation''' - scientific work related to [[Wikipedia quality]] published in 2014, written by [[Takuya Makino]]. | ||
== Overview == | == Overview == | ||
In Targeted Entity Disambiguation setting, authors take (i) a set of entity names which belong to the same domain (target entities), (ii) candidate mentions of the given entities which are texts that contain the target entities as input, and then determine which ones are true mentions of “target entity”. For example, given the names of IT companies, including Apple, authors determine Apple in a mention denotes an IT company or not. Prior work proposed a graph based model. This model ranks all candidate mentions based on scores which denote the degree of relevancy to target entities. Furthermore, this graph based model could utilize reference pages of target entities. However, human annotators must select reference pages in advance. Authors propose an automatic method that can select reference pages. Authors formalize the selection problem of reference pages as an Integer Linear Programming problem. Authors show that model works as well as the prior work that manually selected reference pages. | In Targeted Entity Disambiguation setting, authors take (i) a set of entity names which belong to the same domain (target entities), (ii) candidate mentions of the given entities which are texts that contain the target entities as input, and then determine which ones are true mentions of “target entity”. For example, given the names of IT companies, including Apple, authors determine Apple in a mention denotes an IT company or not. Prior work proposed a graph based model. This model ranks all candidate mentions based on scores which denote the degree of relevancy to target entities. Furthermore, this graph based model could utilize reference pages of target entities. However, human annotators must select reference pages in advance. Authors propose an automatic method that can select reference pages. Authors formalize the selection problem of reference pages as an Integer Linear Programming problem. Authors show that model works as well as the prior work that manually selected reference pages. |
Revision as of 09:26, 18 August 2020
Authors | Takuya Makino |
---|---|
Publication date | 2014 |
DOI | 10.3115/v1/E14-4021 |
Links | Original |
Automatic Selection of Reference Pages in Wikipedia for Improving Targeted Entities Disambiguation - scientific work related to Wikipedia quality published in 2014, written by Takuya Makino.
Overview
In Targeted Entity Disambiguation setting, authors take (i) a set of entity names which belong to the same domain (target entities), (ii) candidate mentions of the given entities which are texts that contain the target entities as input, and then determine which ones are true mentions of “target entity”. For example, given the names of IT companies, including Apple, authors determine Apple in a mention denotes an IT company or not. Prior work proposed a graph based model. This model ranks all candidate mentions based on scores which denote the degree of relevancy to target entities. Furthermore, this graph based model could utilize reference pages of target entities. However, human annotators must select reference pages in advance. Authors propose an automatic method that can select reference pages. Authors formalize the selection problem of reference pages as an Integer Linear Programming problem. Authors show that model works as well as the prior work that manually selected reference pages.