@inproceedings{maheshwari-etal-2018-entity,
title = "Entity Resolution and Location Disambiguation in the {A}ncient {H}indu Temples Domain using Web Data",
author = "Maheshwari, Ayush and
Kumar, Vishwajeet and
Ramakrishnan, Ganesh and
Nath, J. Saketha",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Demonstrations",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-5010",
doi = "10.18653/v1/N18-5010",
pages = "46--50",
abstract = "We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples. Scarce, unstructured information poses a challenge to Entity Resolution(ER) and snippet ranking. Additionally, because the same set of entities may be associated with multiple locations, Location Disambiguation(LD) is a problem. The mentions and descriptions of temples exist in the order of hundreds of thousands, with such data generated by various users in various forms such as text (Wikipedia pages), videos (YouTube videos), blogs, etc. We demonstrate an integrated approach using a combination of grammar rules for parsing and unsupervised (clustering) algorithms to resolve entity and locations with high confidence. A demo of our system is accessible at \url{tinyurl.com/templedemos}. Our system is open source and available on GitHub.",
}
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<abstract>We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples. Scarce, unstructured information poses a challenge to Entity Resolution(ER) and snippet ranking. Additionally, because the same set of entities may be associated with multiple locations, Location Disambiguation(LD) is a problem. The mentions and descriptions of temples exist in the order of hundreds of thousands, with such data generated by various users in various forms such as text (Wikipedia pages), videos (YouTube videos), blogs, etc. We demonstrate an integrated approach using a combination of grammar rules for parsing and unsupervised (clustering) algorithms to resolve entity and locations with high confidence. A demo of our system is accessible at tinyurl.com/templedemos. Our system is open source and available on GitHub.</abstract>
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%0 Conference Proceedings
%T Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data
%A Maheshwari, Ayush
%A Kumar, Vishwajeet
%A Ramakrishnan, Ganesh
%A Nath, J. Saketha
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
%D 2018
%8 jun
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F maheshwari-etal-2018-entity
%X We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples. Scarce, unstructured information poses a challenge to Entity Resolution(ER) and snippet ranking. Additionally, because the same set of entities may be associated with multiple locations, Location Disambiguation(LD) is a problem. The mentions and descriptions of temples exist in the order of hundreds of thousands, with such data generated by various users in various forms such as text (Wikipedia pages), videos (YouTube videos), blogs, etc. We demonstrate an integrated approach using a combination of grammar rules for parsing and unsupervised (clustering) algorithms to resolve entity and locations with high confidence. A demo of our system is accessible at tinyurl.com/templedemos. Our system is open source and available on GitHub.
%R 10.18653/v1/N18-5010
%U https://aclanthology.org/N18-5010
%U https://doi.org/10.18653/v1/N18-5010
%P 46-50
Markdown (Informal)
[Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data](https://aclanthology.org/N18-5010) (Maheshwari et al., NAACL 2018)
ACL