Ultra-fine Entity Typing with Indirect Supervision from Natural Language Inference
Author:
Affiliation:
1. University of Southern California, USA
2. University of Illinois at Urbana-Champaign, USA. vincentleebang@gmail.com
3. Temple University, USA. wenpeng.yin@temple.edu
4. University of Southern California, USA. muhaoche@usc.edu
Abstract
Publisher
MIT Press - Journals
Subject
Artificial Intelligence,Computer Science Applications,Linguistics and Language,Human-Computer Interaction,Communication
Link
https://direct.mit.edu/tacl/article-pdf/doi/10.1162/tacl_a_00479/2022954/tacl_a_00479.pdf
Reference47 articles.
1. A large annotated corpus for learning natural language inference;Bowman,2015
2. Grounding ‘grounding’ in NLP;Chandu,2021
3. Can NLI models verify QA systems’ predictions?;Chen,2021
4. Hierarchical entity typing via multi-level learning to rank;Chen,2020
5. Hierarchical entity typing via multi-level learning to rank;Chen,2020
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1. Knowledge graph-based manufacturing process planning: A state-of-the-art review;Journal of Manufacturing Systems;2023-10
2. Efficient Ultrafine Typing of Named Entities;2023 ACM/IEEE Joint Conference on Digital Libraries (JCDL);2023-06
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