Textual resource acquisition and engineering.
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| Title: | Textual resource acquisition and engineering. |
|---|---|
| Authors: | Chu-Carroll, J.1 jencc@us.ibm.com, Fan, J.1 fanj@us.ibm.com, Schlaefer, N.2 nico@cs.cmu.edu, Zadrozny, W.1 wlodz@us.ibm.com |
| Source: | IBM Journal of Research & Development. May-Jul2012, Vol. 56 Issue 3/4, p4:1-4:11. 11p. |
| Subjects: | Question answering systems, Text Retrieval Conference, Watson (Computer), Transmission of texts, Iterative methods (Mathematics), Wikipedia, Encyclopedias & dictionaries, Web databases |
| Abstract: | A key requirement for high-performing question-answering (QA) systems is access to high-quality reference corpora from which answers to questions can be hypothesized and evaluated. However, the topic of source acquisition and engineering has received very little attention so far. This is because most existing systems were developed under organized evaluation efforts that included reference corpora as part of the task specification. The task of answering Jeopardy!™ questions, on the other hand, does not come with such a well-circumscribed set of relevant resources. Therefore, it became part of the IBM Watson™ effort to develop a set of well-defined procedures to acquire high-quality resources that can effectively support a high-performing QA system. To this end, we developed three procedures, i.e., source acquisition, source transformation, and source expansion. Source acquisition is an iterative development process of acquiring new collections to cover salient topics deemed to be gaps in existing resources based on principled error analysis. Source transformation refers to the process in which information is extracted from existing sources, either as a whole or in part, and is represented in a form that the system can most easily use. Finally, source expansion attempts to increase the coverage in the content of each known topic by adding new information as well as lexical and syntactic variations of existing information extracted from external large collections. In this paper, we discuss the methodology that we developed for IBM Watson for performing acquisition, transformation, and expansion of textual resources. We demonstrate the effectiveness of each technique through its impact on candidate recall and on end-to-end QA performance. [ABSTRACT FROM AUTHOR] |
| Copyright of IBM Journal of Research & Development is the property of IBM Corporation/IBM Journals and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 74605093 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Textual resource acquisition and engineering. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chu-Carroll%2C+J%2E%22">Chu-Carroll, J.</searchLink><relatesTo>1</relatesTo><i> jencc@us.ibm.com</i><br /><searchLink fieldCode="AR" term="%22Fan%2C+J%2E%22">Fan, J.</searchLink><relatesTo>1</relatesTo><i> fanj@us.ibm.com</i><br /><searchLink fieldCode="AR" term="%22Schlaefer%2C+N%2E%22">Schlaefer, N.</searchLink><relatesTo>2</relatesTo><i> nico@cs.cmu.edu</i><br /><searchLink fieldCode="AR" term="%22Zadrozny%2C+W%2E%22">Zadrozny, W.</searchLink><relatesTo>1</relatesTo><i> wlodz@us.ibm.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IBM+Journal+of+Research+%26+Development%22">IBM Journal of Research & Development</searchLink>. May-Jul2012, Vol. 56 Issue 3/4, p4:1-4:11. 11p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Question+answering+systems%22">Question answering systems</searchLink><br /><searchLink fieldCode="DE" term="%22Text+Retrieval+Conference%22">Text Retrieval Conference</searchLink><br /><searchLink fieldCode="DE" term="%22Watson+%28Computer%29%22">Watson (Computer)</searchLink><br /><searchLink fieldCode="DE" term="%22Transmission+of+texts%22">Transmission of texts</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Wikipedia%22">Wikipedia</searchLink><br /><searchLink fieldCode="DE" term="%22Encyclopedias+%26+dictionaries%22">Encyclopedias & dictionaries</searchLink><br /><searchLink fieldCode="DE" term="%22Web+databases%22">Web databases</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: A key requirement for high-performing question-answering (QA) systems is access to high-quality reference corpora from which answers to questions can be hypothesized and evaluated. However, the topic of source acquisition and engineering has received very little attention so far. This is because most existing systems were developed under organized evaluation efforts that included reference corpora as part of the task specification. The task of answering Jeopardy!™ questions, on the other hand, does not come with such a well-circumscribed set of relevant resources. Therefore, it became part of the IBM Watson™ effort to develop a set of well-defined procedures to acquire high-quality resources that can effectively support a high-performing QA system. To this end, we developed three procedures, i.e., source acquisition, source transformation, and source expansion. Source acquisition is an iterative development process of acquiring new collections to cover salient topics deemed to be gaps in existing resources based on principled error analysis. Source transformation refers to the process in which information is extracted from existing sources, either as a whole or in part, and is represented in a form that the system can most easily use. Finally, source expansion attempts to increase the coverage in the content of each known topic by adding new information as well as lexical and syntactic variations of existing information extracted from external large collections. In this paper, we discuss the methodology that we developed for IBM Watson for performing acquisition, transformation, and expansion of textual resources. We demonstrate the effectiveness of each technique through its impact on candidate recall and on end-to-end QA performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IBM Journal of Research & Development is the property of IBM Corporation/IBM Journals and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1147/JRD.2012.2185901 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 11 StartPage: 4:1 Subjects: – SubjectFull: Question answering systems Type: general – SubjectFull: Text Retrieval Conference Type: general – SubjectFull: Watson (Computer) Type: general – SubjectFull: Transmission of texts Type: general – SubjectFull: Iterative methods (Mathematics) Type: general – SubjectFull: Wikipedia Type: general – SubjectFull: Encyclopedias & dictionaries Type: general – SubjectFull: Web databases Type: general Titles: – TitleFull: Textual resource acquisition and engineering. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chu-Carroll, J. – PersonEntity: Name: NameFull: Fan, J. – PersonEntity: Name: NameFull: Schlaefer, N. – PersonEntity: Name: NameFull: Zadrozny, W. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May-Jul2012 Type: published Y: 2012 Identifiers: – Type: issn-print Value: 00188646 Numbering: – Type: volume Value: 56 – Type: issue Value: 3/4 Titles: – TitleFull: IBM Journal of Research & Development Type: main |
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