The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Fine-Grained Entity Recognition
Open accessAbstract
Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g., person, organization, location or miscellaneous. In order to intelligently understand text and extract a wide range of information, it is useful to more precisely determine the semantic classes of entities mentioned in unstructured text. This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes an unsupervised method for collecting training data, and presents the FIGER implementation. Experiments show that the system accurately predicts the tags for entities. Moreover, it provides useful information for a relation extraction system, increasing the F1 score by 93%. We make FIGER and its data available as a resource for future work.
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Toward an architecture for never-ending language learning
Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka, E., & Mitchell, T. (2010) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v24i1.7519 · Cited by 2,000
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Zhang, J., & El-Gohary, N. M. (2016) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000346 · Cited by 414
Cross-lingual Name Tagging and Linking for 282 Languages
Pan, X., Zhang, B., May, J., Nothman, J., Knight, K., & Ji, H. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1178 · Cited by 312
Sources of Hallucination by Large Language Models on Inference Tasks
McKenna, N., Li, T., Cheng, L., Hosseini, M., Johnson, M., & Steedman, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023 · doi:10.18653/v1/2023.findings-emnlp.182 · Cited by 142
Metadata
- Authors
- Ling, X., & Weld, D.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2021-09-20
- Volume
- 26
- Issue
- 1
- Pages
- 94-100
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2406945108
- Cited by
- 476
- References
- 4
- FWCI
- 18.51
- Type
- conference paper
- Language
- en
- Open access
- diamond
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