Moosavi, Nafise Sadat ; Born, Leo ; Poesio, Massimo ; Strube, Michael (2019)
Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection.
The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Florence, Italy (28.07.2019-02.08.2019)
doi: 10.18653/v1/P19-1408
Conference or Workshop Item, Bibliographie
This is the latest version of this item.
Abstract
The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment. To address this problem, minimum spans are manually annotated in smaller corpora. However, this additional annotation is costly and therefore, this solution does not scale to large corpora. In this paper, we propose the MINA algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. We show that the extracted minimum spans by MINA are consistent with those that are manually annotated by experts. Our experiments show that using minimum spans is in particular important in cross-dataset coreference evaluation, in which detected mention boundaries are noisier due to domain shift. We have integrated MINA into https://github.com/ns-moosavi/coval for reporting standard coreference scores based on both maximum and automatically detected minimum spans.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2019 |
| Creators: | Moosavi, Nafise Sadat ; Born, Leo ; Poesio, Massimo ; Strube, Michael |
| Type of entry: | Bibliographie |
| Title: | Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection |
| Language: | English |
| Date: | 27 May 2019 |
| Place of Publication: | Florence, Italy |
| Publisher: | Association for Computational Linguistics |
| Book Title: | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics |
| Event Title: | The 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019) |
| Event Location: | Florence, Italy |
| Event Dates: | 28.07.2019-02.08.2019 |
| DOI: | 10.18653/v1/P19-1408 |
| URL / URN: | https://aclanthology.org/P19-1408 |
| Corresponding Links: | |
| Abstract: | The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepositional phrase attachment. To address this problem, minimum spans are manually annotated in smaller corpora. However, this additional annotation is costly and therefore, this solution does not scale to large corpora. In this paper, we propose the MINA algorithm for automatically extracting minimum spans to benefit from minimum span evaluation in all corpora. We show that the extracted minimum spans by MINA are consistent with those that are manually annotated by experts. Our experiments show that using minimum spans is in particular important in cross-dataset coreference evaluation, in which detected mention boundaries are noisier due to domain shift. We have integrated MINA into https://github.com/ns-moosavi/coval for reporting standard coreference scores based on both maximum and automatically detected minimum spans. |
| Uncontrolled Keywords: | UKP_p_QAEduInf |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Ubiquitous Knowledge Processing DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources |
| Date Deposited: | 18 Sep 2019 12:20 |
| Last Modified: | 05 Jun 2024 07:53 |
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| Suche nach Titel in: | TUfind oder in Google |
Available Versions of this Item
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Handling Domain Shift in Coreference Evaluation by Using Automatically Extracted Minimum Spans. (deposited 27 May 2019 13:57)
- Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection. (deposited 18 Sep 2019 12:20) [Currently Displayed]
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