Difference between revisions of "Minimally-Supervised Extraction of Domain-Specific Part-Whole Relations Using Wikipedia as Knowledge-Base"
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| + | | title = Minimally-Supervised Extraction of Domain-Specific Part-Whole Relations Using Wikipedia as Knowledge-Base | ||
| + | | date = 2013 | ||
| + | | authors = [[Ashwin Ittoo]]<br />[[Gosse Bouma]] | ||
| + | | doi = 10.1016/j.datak.2012.06.004 | ||
| + | | link = http://www.sciencedirect.com/science/article/pii/S0169023X12000638 | ||
| + | }} | ||
'''Minimally-Supervised Extraction of Domain-Specific Part-Whole Relations Using Wikipedia as Knowledge-Base''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Ashwin Ittoo]] and [[Gosse Bouma]]. | '''Minimally-Supervised Extraction of Domain-Specific Part-Whole Relations Using Wikipedia as Knowledge-Base''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Ashwin Ittoo]] and [[Gosse Bouma]]. | ||
== Overview == | == Overview == | ||
Authors present a minimally-supervised approach for learning part-whole relations from texts. Unlike previous techniques, authors focused on sparse, domain-specific texts. The novelty in approach lies in the use of [[Wikipedia]] as a knowledge-base, from which authors first acquire a set of reliable patterns that express part-whole relations. This is achieved by a minimally-supervised algorithm. Authors then use the patterns acquired to extract part-whole relation triples from a collection of sparse, domain-specific texts. Authors strategy, of learning in one domain and applying the knowledge in another domain is based upon the notion of domain-adaption. It allows us to overcome the challenges of learning the relations directly from the sparse, domain-specific corpus. Authors experimental evaluations reveal that, despite its general-purpose nature, Wikipedia can be exploited as a source of knowledge for improving the performance of domain-specific part-whole relation extraction. As other contributions, authors propose a mechanism that mitigates the negative impact of semantic-drift on minimally-supervised algorithms. Also, authors represent the patterns in the extracted relations using sophisticated syntactic structures that avoid the limitations of traditional surface string representations. In addition, authors show that domain-specific part-whole relations cannot be conclusively classified in existing taxonomies. | Authors present a minimally-supervised approach for learning part-whole relations from texts. Unlike previous techniques, authors focused on sparse, domain-specific texts. The novelty in approach lies in the use of [[Wikipedia]] as a knowledge-base, from which authors first acquire a set of reliable patterns that express part-whole relations. This is achieved by a minimally-supervised algorithm. Authors then use the patterns acquired to extract part-whole relation triples from a collection of sparse, domain-specific texts. Authors strategy, of learning in one domain and applying the knowledge in another domain is based upon the notion of domain-adaption. It allows us to overcome the challenges of learning the relations directly from the sparse, domain-specific corpus. Authors experimental evaluations reveal that, despite its general-purpose nature, Wikipedia can be exploited as a source of knowledge for improving the performance of domain-specific part-whole relation extraction. As other contributions, authors propose a mechanism that mitigates the negative impact of semantic-drift on minimally-supervised algorithms. Also, authors represent the patterns in the extracted relations using sophisticated syntactic structures that avoid the limitations of traditional surface string representations. In addition, authors show that domain-specific part-whole relations cannot be conclusively classified in existing taxonomies. | ||
Revision as of 10:03, 15 January 2020
Authors | Ashwin Ittoo Gosse Bouma |
|---|---|
Publication date | 2013 |
DOI | 10.1016/j.datak.2012.06.004 |
Links | Original |
Minimally-Supervised Extraction of Domain-Specific Part-Whole Relations Using Wikipedia as Knowledge-Base - scientific work related to Wikipedia quality published in 2013, written by Ashwin Ittoo and Gosse Bouma.
Overview
Authors present a minimally-supervised approach for learning part-whole relations from texts. Unlike previous techniques, authors focused on sparse, domain-specific texts. The novelty in approach lies in the use of Wikipedia as a knowledge-base, from which authors first acquire a set of reliable patterns that express part-whole relations. This is achieved by a minimally-supervised algorithm. Authors then use the patterns acquired to extract part-whole relation triples from a collection of sparse, domain-specific texts. Authors strategy, of learning in one domain and applying the knowledge in another domain is based upon the notion of domain-adaption. It allows us to overcome the challenges of learning the relations directly from the sparse, domain-specific corpus. Authors experimental evaluations reveal that, despite its general-purpose nature, Wikipedia can be exploited as a source of knowledge for improving the performance of domain-specific part-whole relation extraction. As other contributions, authors propose a mechanism that mitigates the negative impact of semantic-drift on minimally-supervised algorithms. Also, authors represent the patterns in the extracted relations using sophisticated syntactic structures that avoid the limitations of traditional surface string representations. In addition, authors show that domain-specific part-whole relations cannot be conclusively classified in existing taxonomies.
