Domain-independent sentence type classification: examining the scenarios of scientific abstracts and scrum protocols
Article No.: 5, Pages 1 - 8
Abstract
The amount of available textual information in everybody's daily environment is increasing steadily. To satisfy a user's information needs, the user has to examine numerous documents until the required information has been found. Additionally, the relevant information is often contained in only short sections of the considered documents. This leads to a high amount of irrelevant text the user has to read what could be solved by filtering relevant information within textual documents automatically. In this article we present our findings on the classification of sentences according to the type of information contained. Our evaluation has been conducted on documents from the field of abstracts of scientific publications and protocols of Scrum retrospective meetings. The results show the feasibility of our approach for finding a higher percentage of relevant information within textual documents and hence reducing the information overload for the users.
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- Domain-independent sentence type classification: examining the scenarios of scientific abstracts and scrum protocols
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September 2014
262 pages
ISBN:9781450327695
DOI:10.1145/2637748
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Published: 16 September 2014
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i-KNOW '14
i-KNOW '14: 14th International Conference on Knowledge Technologies and Data-driven Business
September 16 - 19, 2014
Graz, Austria
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i-KNOW '14 Paper Acceptance Rate 25 of 73 submissions, 34%;
Overall Acceptance Rate 77 of 238 submissions, 32%
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