Coreference Resolution
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Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
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I voted for Obama because he was most aligned with my values", she said.
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"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/google-research/google-research
공식 코드https://github.com/google-research/language/tree/master/language/multiberts
공식 코드https://github.com/gucorpling/amalgum
공식 코드https://github.com/gucorpling/gentle
공식 코드GENTLE: A Genre-Diverse Multilayer Challenge Set for English NLP and Linguistic Evaluation
https://github.com/gvanboven/transforming_dutch
공식 코드Transforming Dutch: Debiasing Dutch Coreference Resolution Systems for Non-binary Pronouns
https://github.com/haixiachai/multi-coref
공식 코드Investigating Multilingual Coreference Resolution by Universal Annotations
https://github.com/hamid-amir/CueWords
공식 코드https://github.com/hazyresearch/ama_prompting
공식 코드Ask Me Anything: A simple strategy for prompting language models
https://github.com/hazyresearch/h3
공식 코드Hungry Hungry Hippos: Towards Language Modeling with State Space Models
https://github.com/helw150/event_entity_coref_ecb_plus
공식 코드Focus on what matters: Applying Discourse Coherence Theory to Cross Document Coreference
https://github.com/hkust-knowcomp/exo-pcr
공식 코드Exophoric Pronoun Resolution in Dialogues with Topic Regularization
https://github.com/hkust-knowcomp/vd-pcr
공식 코드VD-PCR: Improving Visual Dialog with Pronoun Coreference Resolution
https://github.com/hwazni/qcoref
공식 코드Towards Transparency in Coreference Resolution: A Quantum-Inspired Approach
https://github.com/i-need-sleep/mmcoref_cleaned
공식 코드UNITER-Based Situated Coreference Resolution with Rich Multimodal Input
https://github.com/ianporada/challenge-set-assumption
공식 코드https://github.com/ianporada/coref-data
공식 코드https://github.com/ianporada/coref-reeval
공식 코드https://github.com/ibm/docamr
공식 코드https://github.com/idea-ccnl/fengshenbang-lm
공식 코드Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective
https://github.com/informagi/conversational-entity-linking-2022
공식 코드Personal Entity, Concept, and Named Entity Linking in Conversations
https://github.com/internlm/huixiangdou
공식 코드https://github.com/jchiyah/exloring-mm-in-simmc2
공식 코드https://github.com/jchiyah/exploring-mm-in-simmc2
공식 코드https://github.com/jiyfeng/entitynlm
공식 코드https://github.com/joeljang/elm
공식 코드Exploring the Benefits of Training Expert Language Models over Instruction Tuning
https://github.com/johngiorgi/seq2rel
공식 코드A sequence-to-sequence approach for document-level relation extraction
https://github.com/jsksxs360/prompt-event-coref-emnlp2023
공식 코드https://github.com/judicaelpoumay/event_entity_coref_ecb_plus
공식 코드A Comprehensive Comparison of Word Embeddings in Event & Entity Coreference Resolution
https://github.com/juntaoy/aracoref
공식 코드https://github.com/juntaoy/dali-bridging
공식 코드Multi-task Learning Based Neural Bridging Reference Resolution
https://github.com/juntaoy/dali-full-anaphora
공식 코드https://github.com/juntaoy/dali-md
공식 코드https://github.com/juntaoy/dali-plural
공식 코드Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution
https://github.com/kaist-lklab/cot-collection
공식 코드https://github.com/kaistai/cot-collection
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.