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NTUNLP approaches to recognizing and disambiguating entities in long and short text at the ERD challenge 2014
2014
Proceedings of the first international workshop on Entity recognition & disambiguation - ERD '14
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This paper presents the NTUNLP systems in the long track and the short track of the Entity Recognition and Disambiguation Challenge 2014. ...
In the formal run, one NTUNLP system wins the first prize in the short track and another NTUNLP system gets the fourth place in the long track. ...
CONCLUSION AND FUTURE WORKS In this paper, we introduce the NTUNLP approaches to recognizing and disambiguating entities appearing in the long and in the short text. ...
doi:10.1145/2633211.2634363
dblp:conf/sigir/ChiuSLSCWC14
fatcat:lvq5opxyxbdoljz7oe3wiywv6a
Tulip
2014
Proceedings of the first international workshop on Entity recognition & disambiguation - ERD '14
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This article presents Tulip, an ERD system submitted to the ERD 2014: Entity Recognition and Disambiguation Challenge. ...
The objective of the proposed system is to spot mentions of entities in a document and link the mentions to corresponding Freebase articles. ...
Additional project resources can be found at: https://web.cs.dal.ca/~lipczak/erd/
ACKNOWLEDGMENTS We would like to thank Axel Soto, Armin Sajadi, Seyednaser Nourashrafeddin and Krzysztof Lipczak for ...
doi:10.1145/2633211.2634351
dblp:conf/sigir/LipczakKM14
fatcat:gacer2ag6fflxf3m2h3iz7xnza
Entity Linking for Queries by Searching Wikipedia Sentences
2017
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
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The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. ...
The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. ...
Acknowledgments We thank Ming-Wei Chang for sharing the ERD14 dataset. Chuanqi Tan and Weifeng Lv are supported by the National Natural Science Foundation of China (Grant No. 61421003). ...
doi:10.18653/v1/d17-1007
dblp:conf/emnlp/TanWRLZ17
fatcat:wswe25vt7rdjpmsuwubdekn5aa
Entity Linking for Queries by Searching Wikipedia Sentences
[article]
2017
arXiv
pre-print
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The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. ...
The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. ...
Acknowledgments We thank Ming-Wei Chang for sharing the ERD14 dataset. The first author and the fourth author are supported by the National Natural Science Foundation of China (Grant No. 61421003). ...
arXiv:1704.02788v3
fatcat:45vk5cqd2rcjrmr7lpzonyx4ke
A Piggyback System for Joint Entity Mention Detection and Linking in Web Queries
2016
Proceedings of the 25th International Conference on World Wide Web - WWW '16
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The key algorithmic idea underlying SMAPH-2 is to first discover a candidate set of entities and then link-back those entities to their mentions occurring in the input query. ...
We introduce SMAPH-2, a second-order approach that, by piggybacking on a web search engine, alleviates the noise and irregularities that characterize the language of queries and puts queries in a larger ...
The former task was the one addressed by the participants of the 2014 ERD Challenge, the latter is the one typically addressed in (short and long) texts. Queries can be inherently ambiguous. ...
doi:10.1145/2872427.2883061
dblp:conf/www/CornoltiFCRS16
fatcat:llvnk3ivzrbjpbftuapbrwlzyi