nlp:coreference_resolution

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nlp:coreference_resolution [2021/09/08 18:32] – [Datasets] jmflanignlp:coreference_resolution [2024/01/04 23:21] (current) – [Papers] jmflanig
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 ===== Papers ===== ===== Papers =====
-  * [[https://cs224d.stanford.edu/reports/ClarkKevin.pdf|Clark 2016]] (Kevin Clark's preliminary work) +  * Neural Methods 
-  * [[https://arxiv.org/pdf/1606.01323.pdf|Clark & Manning 2016 - Improving Coreference Resolution by Learning Entity-Level Distributed Representations]] +    * [[https://cs224d.stanford.edu/reports/ClarkKevin.pdf|Clark 2016]] (Kevin Clark's preliminary work) 
-  * [[https://arxiv.org/pdf/1609.08667.pdf|Clark & Manning 2016 - Deep Reinforcement Learning for Mention-Ranking Coreference Models]] +    * [[https://arxiv.org/pdf/1606.01323.pdf|Clark & Manning 2016 - Improving Coreference Resolution by Learning Entity-Level Distributed Representations]] 
-  * [[https://arxiv.org/pdf/1707.07045.pdf|Lee et al 2017 - End-to-end Neural Coreference Resolution]] +    * [[https://arxiv.org/pdf/1609.08667.pdf|Clark & Manning 2016 - Deep Reinforcement Learning for Mention-Ranking Coreference Models]] 
-  * [[https://arxiv.org/abs/1804.05392|Lee et al 2018 - Higher-order Coreference Resolution with Coarse-to-fine Inference]] +    * [[https://arxiv.org/pdf/1707.07045.pdf|Lee et al 2017 - End-to-end Neural Coreference Resolution]] 
-  * [[https://arxiv.org/pdf/2009.12013.pdf|Xu & Choi 2020 - Revealing the Myth of Higher-Order Inference in Coreference Resolution]]+    * [[https://arxiv.org/abs/1804.05392|Lee et al 2018 - Higher-order Coreference Resolution with Coarse-to-fine Inference]] 
 +    * [[https://arxiv.org/pdf/2009.12013.pdf|Xu & Choi 2020 - Revealing the Myth of Higher-Order Inference in Coreference Resolution]] 
 +    * [[https://arxiv.org/pdf/2101.00434.pdf|Kirstain et al 2021 - Coreference Resolution without Span Representations]] 
 +    * [[https://arxiv.org/pdf/2205.12644.pdf|Otmazgin et al 2023 - LINGMESS: Linguistically Informed Multi Expert Scorers for Coreference Resolution]] 
 +  * Prompting Methods 
 +    * [[https://aclanthology.org/2020.acl-main.622.pdf|Wu et al 2020 - CorefQA: Coreference Resolution as Query-based Span Prediction]] 
 +    * [[https://arxiv.org/pdf/2305.14489.pdf|Le & Ritter 2023 - Are Large Language Models Robust Zero-shot Coreference Resolvers?]] See their prompt in the appendix (Table 10) 
 + 
 +==== Event Coreference ==== 
 +  * Overviews 
 +    * [[https://www.hlt.utdallas.edu/~vince/papers/ijcai18-ecoref.pdf|Event Coreference Resolution: A Survey of Two Decades of Research]] 
 +  * Papers 
 +    * [[https://aclanthology.org/D12-1045.pdf|Lee et al 2012 - Joint Entity and Event Coreference Resolution across Documents]] 
 +    * [[https://aclanthology.org/2020.coling-main.275.pdf|Zheng et al 2020 - Event Coreference Resolution with their Paraphrases and Argument-aware Embeddings]]
  
 ===== Datasets ===== ===== Datasets =====
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   * Winobias dataset: [[https://arxiv.org/pdf/1804.06876.pdf|paper]]   * Winobias dataset: [[https://arxiv.org/pdf/1804.06876.pdf|paper]]
   * GAP dataset:   * GAP dataset:
 +  * BUG dataset: [[https://arxiv.org/pdf/2109.03858.pdf|Levy et al 2021 - Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation]]
  
 ===== Metric ===== ===== Metric =====
nlp/coreference_resolution.1631125936.txt.gz · Last modified: 2023/06/15 07:36 (external edit)

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