https://github.com/rahular/joint-coref-srl
공식 코드Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards
Papers with Code 아카이브
Coreference Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 16
- 보존된 평가 기록
- 154
Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
+-----------+
| |
I voted for Obama because he was most aligned with my values", she said.
| | |
+-------------------------------------------------+------------+
"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/rahular/model-based-coref
공식 코드https://github.com/rakeshchada/corefqa
공식 코드Gendered Pronoun Resolution using BERT and an extractive question answering formulation
https://github.com/ritwikmishra/indicgenqa
공식 코드Long-context Non-factoid Question Answering in Indic Languages
https://github.com/ritwikmishra/transmucores
공식 코드Multilingual Coreference Resolution in Low-resource South Asian Languages
https://github.com/rudinger/winogender-schemas
공식 코드https://github.com/sahithyaravi/comet-m
공식 코드COMET-M: Reasoning about Multiple Events in Complex Sentences
https://github.com/sahithyaravi1493/coref_csk
공식 코드What happens before and after: Multi-Event Commonsense in Event Coreference Resolution
https://github.com/salesforce/UniversalFewShotNLP
공식 코드https://github.com/sapienzanlp/maverick-coref
공식 코드Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends
https://github.com/sattree/gap
공식 코드Gendered Ambiguous Pronouns Shared Task: Boosting Model Confidence by Evidence Pooling
https://github.com/satwikkottur/clevr-dialog
공식 코드CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog
https://github.com/sayantan11995/event-timeline-generation-from-documents
공식 코드Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach
https://github.com/seonghyeonye/flipped-learning
공식 코드Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
https://github.com/sfu-discourse-lab/gendergaptracker
공식 코드Radar de Parité: An NLP system to measure gender representation in French news stories
https://github.com/shadmanrohan/bencoref
공식 코드BenCoref: A Multi-Domain Dataset of Nominal Phrases and Pronominal Reference Annotations
https://github.com/shirapp/coreference-based-ontology-building
공식 코드https://github.com/shon-otmazgin/fastcoref
공식 코드F-coref: Fast, Accurate and Easy to Use Coreference Resolution
https://github.com/shon-otmazgin/lingmess-coref
공식 코드LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
https://github.com/shtoshni92/fast-coref
공식 코드https://github.com/shtoshni92/long-doc-coref
공식 코드Learning to Ignore: Long Document Coreference with Bounded Memory Neural Networks
https://github.com/shtoshni92/petra
공식 코드PeTra: A Sparsely Supervised Memory Model for People Tracking
https://github.com/shtoshni92/span-rep
공식 코드https://github.com/sinadumi/mmrr
공식 코드https://github.com/slab-nlp/bug
공식 코드Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation
https://github.com/slab-nlp/cog-gb-eval
공식 코드https://github.com/smartschat/cort
공식 코드https://github.com/stanfordnlp/stanza
공식 코드Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/statsmaths/cleanNLP
공식 코드A Tidy Data Model for Natural Language Processing using cleanNLP
https://github.com/storidient/koconovel
공식 코드KoCoNovel: Annotated Dataset of Character Coreference in Korean Novels
https://github.com/swabhs/scaffolding
공식 코드https://github.com/swiseman/nn_coref
공식 코드https://github.com/taolusi/secure
공식 코드https://github.com/tchewik/corefhd
공식 코드Light Coreference Resolution for Russian with Hierarchical Discourse Features
https://github.com/tensorflow/lingvo
공식 코드https://github.com/tensorflow/mesh
공식 코드ST-MoE: Designing Stable and Transferable Sparse Expert Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.