NDLI: From TagME to WAT: a new entity annotator

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From TagME to WAT: a new entity annotator

Content Provider	ACM Digital Library
Author	Piccinno, Francesco Ferragina, Paolo
Abstract	In this paper we propose a novel entity annotator for texts which hinges on TagME's algorithmic technology, currently the best one available. The novelty is twofold: from the one hand, we have engineered the software in order to be modular and more efficient; from the other hand, we have improved the annotation pipeline by re-designing all of its three main modules: spotting, disambiguation and pruning. In particular, the re-design has involved the detailed inspection of the performance of these modules by developing new algorithms which have been in turn tested over all publicly available datasets (i.e. AIDA, IITB, MSN, AQUAINT, and the one of the ERD Challenge). This extensive experimentation allowed us to derive the best combination which achieved on the ERD development dataset an F1 score of 74.8%, which turned to be 67.2% F1 for the test dataset. This final result was due to an impressive precision equal to 87.6%, but very low recall 54.5%. With respect to classic TagME on the development dataset the improvement ranged from 1% to 9% on the D2W benchmark, depending on the disambiguation algorithm being used. As a side result, the final software can be interpreted as a flexible library of several parsing/disambiguation and pruning modules that can be used to build up new and more sophisticated entity annotators. We plan to release our library to the public as an open-source project.
Starting Page	55
Ending Page	62
Page Count	8
File Format	PDF
ISBN	9781450330237
DOI	10.1145/2633211.2634350
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-07-11
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Wikipedia Entity annotation Graph-based algorithms Tagme
Content Type	Text
Resource Type	Article

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