NDLI: Ackseer: a repository and search engine for automatically extracted acknowledgments from digital libraries.

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Ackseer: a repository and search engine for automatically extracted acknowledgments from digital libraries.

Content Provider	CiteSeerX
Author	Khabsa, Madian Treeratpituk, Pucktada Giles, C. Lee
Abstract	Acknowledgments are widely used in scientific articles to express gratitude and credit collaborators. Despite suggestions that indexing acknowledgments automatically will give interesting insights [9], there is currently, to the best of our knowledge, no such system to track acknowledgments and index them 1. In this paper we introduce AckSeer 2,asearch engine and a repository for automatically extracted acknowledgments in digital libraries. AckSeer is a fully automated system that scans items in digital libraries including conference papers, journals, and books extracting acknowledgment sections and identifying acknowledged entities mentioned within. We describe the architecture of AckSeer and discuss the extraction algorithms that achieve a F1 measure above 83%. We use multiple Named Entity Recognition (NER) tools and propose a method for merging the outcome from different recognizers. The resulting entities are stored in a database then made searchable by adding them to the AckSeer index along with the metadata of the containing paper/book. We buildAckSeer on top of the documents in CiteSeerx digital library yielding more than 500,000 acknowledgments and more than 4 million mentioned entities.
File Format	PDF
Access Restriction	Open
Subject Keyword	Digital Library Automatically Extracted Acknowledgment Search Engine F1 Measure Credit Collaborator Different Recognizers Paper Book Interesting Insight Mentioned Entity Extracted Acknowledgment Citeseerx Digital Library Resulting Entity Acknowledgment Section Ackseer Index Automated System Multiple Named Entity Recognition Extraction Algorithm Acknowledged Entity Asearch Engine Scientific Article
Content Type	Text
Resource Type	Article

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