@inproceedings{akasaki-etal-2021-fine-grained,
title = "Fine-grained Typing of Emerging Entities in Microblogs",
author = "Akasaki, Satoshi and
Yoshinaga, Naoki and
Toyoda, Masashi",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.399",
doi = "10.18653/v1/2021.findings-emnlp.399",
pages = "4667--4679",
abstract = "Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film). We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type {`}homographic{'} emerging entities (e.g., {`}Go{'} means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity. Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.",
}
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<abstract>Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film). We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type ‘homographic’ emerging entities (e.g., ‘Go’ means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity. Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.</abstract>
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%0 Conference Proceedings
%T Fine-grained Typing of Emerging Entities in Microblogs
%A Akasaki, Satoshi
%A Yoshinaga, Naoki
%A Toyoda, Masashi
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F akasaki-etal-2021-fine-grained
%X Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation. To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film). We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog. The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity. To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision. We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. To type ‘homographic’ emerging entities (e.g., ‘Go’ means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity. Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.
%R 10.18653/v1/2021.findings-emnlp.399
%U https://aclanthology.org/2021.findings-emnlp.399
%U https://doi.org/10.18653/v1/2021.findings-emnlp.399
%P 4667-4679
Markdown (Informal)
[Fine-grained Typing of Emerging Entities in Microblogs](https://aclanthology.org/2021.findings-emnlp.399) (Akasaki et al., Findings 2021)
ACL
- Satoshi Akasaki, Naoki Yoshinaga, and Masashi Toyoda. 2021. Fine-grained Typing of Emerging Entities in Microblogs. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4667–4679, Punta Cana, Dominican Republic. Association for Computational Linguistics.