https://github.com/derekmma/dialogue-discourse-relation
공식 코드Implicit Discourse Relation Identification for Open-domain Dialogues
Papers with Code 아카이브
Relation Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 8
- 보존된 평가 기록
- 33
Relation Classification is the task of identifying the semantic relation holding between two nominal entities in text.
Source: Structure Regularized Neural Network for Entity Relation Classification for Chinese Literature Text
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/dfki-nlp/meffi-prompt
공식 코드Multilingual Relation Classification via Efficient and Effective Prompting
https://github.com/dfki-nlp/weighting-schemes-report
공식 코드Why only Micro-F1? Class Weighting of Measures for Relation Classification
https://github.com/facebookresearch/SpanBERT
공식 코드SpanBERT: Improving Pre-training by Representing and Predicting Spans
https://github.com/fduyjx/rsman
공식 코드Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction
https://github.com/foxlf823/sodner
공식 코드A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
https://github.com/gjiaying/zs-ska
공식 코드Prompt-based Zero-shot Relation Extraction with Semantic Knowledge Augmentation
https://github.com/gjiaying/zslrc
공식 코드https://github.com/guanw-pku/oed
공식 코드OED: Towards One-stage End-to-End Dynamic Scene Graph Generation
https://github.com/gucorpling/discodisco
공식 코드https://github.com/gucorpling/gum2pdtb
공식 코드https://github.com/guoqianyu-alberta/rankdnn
공식 코드https://github.com/hanjiale/genpt
공식 코드https://github.com/hclent/creoleval
공식 코드https://github.com/heidelberg-nlp/graphlanguagemodels
공식 코드https://github.com/humanlab/rare-class-AL
공식 코드Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class Challenge
https://github.com/ibm/zshot
공식 코드Description Boosting for Zero-Shot Entity and Relation Classification
https://github.com/jackboyla/glirel
공식 코드GLiREL -- Generalist Model for Zero-Shot Relation Extraction
https://github.com/jenlindadsouza/NLPContributions
공식 코드https://github.com/jiaxing-star/facialdynamic
공식 코드https://github.com/jiyfeng/drlm
공식 코드A Latent Variable Recurrent Neural Network for Discourse Relation Language Models
https://github.com/liuwei1206/connrel
공식 코드https://github.com/ljynlp/w2ner
공식 코드Unified Named Entity Recognition as Word-Word Relation Classification
https://github.com/luka-group/unist
공식 코드https://github.com/mainlp/crossre
공식 코드https://github.com/mainlp/discourse_probes
공식 코드Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set
https://github.com/markus-eberts/spert
공식 코드Span-based Joint Entity and Relation Extraction with Transformer Pre-training
https://github.com/maryambrj/alien
공식 코드Comparative Analysis of AI Agent Architectures for Entity Relationship Classification
https://github.com/mgormley/pacaya
공식 코드Improved Relation Extraction with Feature-Rich Compositional Embedding Models
https://github.com/mslars/ace_preprocessing
공식 코드Explaining Relation Classification Models with Semantic Extents
https://github.com/mslars/semantic_extents
공식 코드Explaining Relation Classification Models with Semantic Extents
https://github.com/nicpopovic/fredo
공식 코드https://github.com/ntunlp/kmlm
공식 코드Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
https://github.com/ofirarviv/improving-ud
공식 코드On the Relation between Syntactic Divergence and Zero-Shot Performance
https://github.com/phosseini/causal-reasoning
공식 코드Knowledge-Augmented Language Models for Cause-Effect Relation Classification
https://github.com/pidinosaur2804/wave-cre-plus-plus
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.