Difference between revisions of "Bootstrapping Multilingual Relation Discovery Using English Wikipedia and Wikimedia-Induced Entity Extraction"
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| − | '''Bootstrapping Multilingual Relation Discovery Using English Wikipedia and Wikimedia-Induced Entity Extraction''' - scientific work related to Wikipedia quality published in 2011, written by Patrick Schone, Tim Allison, Chris Giannella and Craig Pfeifer. | + | '''Bootstrapping Multilingual Relation Discovery Using English Wikipedia and Wikimedia-Induced Entity Extraction''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Patrick Schone]], [[Tim Allison]], [[Chris Giannella]] and [[Craig Pfeifer]]. |
== Overview == | == Overview == | ||
| − | Relation extraction has been a subject of significant study over the past decade. Most relation extractors have been developed by combining the training of complex computational systems on large volumes of annotations with extensive rule writing by language experts. Moreover, many relation extractors are reliant on other non-trivial NLP technologies which themselves are developed through significant human efforts, such as entity tagging, parsing, etc. Due to the high cost of creating and assembling the required resources, relation extractors have typically been developed for only high-resourced languages. In this paper, authors describe a near-zero-cost methodology to build relation extractors for significantly distinct non-English languages using only freely available Wikipedia and other web documents, and some knowledge of English. Authors apply methodology and build alma-mater, birthplace, father, occupation, and spouse relation extractors in Greek, Spanish, Russian, and Chinese. Authors conduct evaluations of induced relations at the file level which are the most refined authors have seen in the literature. | + | Relation extraction has been a subject of significant study over the past decade. Most relation extractors have been developed by combining the training of complex computational systems on large volumes of annotations with extensive rule writing by language experts. Moreover, many relation extractors are reliant on other non-trivial NLP technologies which themselves are developed through significant human efforts, such as entity tagging, parsing, etc. Due to the high cost of creating and assembling the required resources, relation extractors have typically been developed for only high-resourced languages. In this paper, authors describe a near-zero-cost methodology to build relation extractors for significantly distinct non-English languages using only freely available [[Wikipedia]] and other web documents, and some knowledge of English. Authors apply methodology and build alma-mater, birthplace, father, occupation, and spouse relation extractors in Greek, Spanish, Russian, and Chinese. Authors conduct evaluations of induced relations at the file level which are the most refined authors have seen in the literature. |
Revision as of 07:55, 25 June 2020
Bootstrapping Multilingual Relation Discovery Using English Wikipedia and Wikimedia-Induced Entity Extraction - scientific work related to Wikipedia quality published in 2011, written by Patrick Schone, Tim Allison, Chris Giannella and Craig Pfeifer.
Overview
Relation extraction has been a subject of significant study over the past decade. Most relation extractors have been developed by combining the training of complex computational systems on large volumes of annotations with extensive rule writing by language experts. Moreover, many relation extractors are reliant on other non-trivial NLP technologies which themselves are developed through significant human efforts, such as entity tagging, parsing, etc. Due to the high cost of creating and assembling the required resources, relation extractors have typically been developed for only high-resourced languages. In this paper, authors describe a near-zero-cost methodology to build relation extractors for significantly distinct non-English languages using only freely available Wikipedia and other web documents, and some knowledge of English. Authors apply methodology and build alma-mater, birthplace, father, occupation, and spouse relation extractors in Greek, Spanish, Russian, and Chinese. Authors conduct evaluations of induced relations at the file level which are the most refined authors have seen in the literature.
