https://github.com/kuehnram/findyourfigure
공식 코드Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration
Papers with Code 아카이브
Argument Mining
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 6
- 평가표
- 1
- 보존된 평가 기록
- 1
Argument Mining is a field of corpus-based discourse analysis that involves the automatic identification of argumentative structures in text.
Source: AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lcs2-iiitd/desyr-cikm-2021
공식 코드DESYR: Definition and Syntactic Representation Based Claim Detection on the Web
https://github.com/liyingcheng95/iam
공식 코드IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
https://github.com/lt-nlp-lab-unibo/bert-natural-explanations
공식 코드https://github.com/manavkapadnis/enigma_argmining
공식 코드Team Enigma at ArgMining-EMNLP 2021: Leveraging Pre-trained Language Models for Key Point Matching
https://github.com/meiqiguo/argmining2022-imagearg
공식 코드ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining
https://github.com/naston/stableremaster
공식 코드Stable Remaster: Bridging the Gap Between Old Content and New Displays
https://github.com/nkees/active-learning-argument-strength
공식 코드https://github.com/parskatt/dedode
공식 코드DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector
https://github.com/parskatt/roma
공식 코드https://github.com/plkumjorn/GrASP
공식 코드GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
https://github.com/ragerri/antidote-projections
공식 코드https://github.com/raruidol/budget-am
공식 코드https://github.com/roznn/dec
공식 코드https://github.com/serenayj/ABCD-ACL2021
공식 코드ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences
https://github.com/serenayj/DeSSE
공식 코드ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences
https://github.com/tchewik/e2e-microtexts
공식 코드End-to-End Argument Mining over Varying Rhetorical Structures
https://github.com/tomatenmarc/taco
공식 코드https://github.com/trtm/ABAM
공식 코드https://github.com/trtm/AURC
공식 코드https://github.com/trusthlt/mining-legal-arguments
공식 코드https://github.com/tuhinjubcse/AMPERSAND-EMNLP2019
공식 코드AMPERSAND: Argument Mining for PERSuAsive oNline Discussions
https://github.com/uhh-lt/comparative
공식 코드https://github.com/ukplab/acl2024-ood-compuational-argumentation
공식 코드https://github.com/ukplab/argument-topic-diversity
공식 코드https://github.com/vamsi995/paraphrase-generator
공식 코드https://github.com/vene/marseille
공식 코드https://github.com/veranchos/argmining_tweets
공식 코드https://github.com/vrcmf/dmon
공식 코드DMON: A Simple yet Effective Approach for Argument Structure Learning
https://github.com/warnikchow/sae4k
공식 코드https://github.com/webis-de/argmining-21
공식 코드Key Point Analysis via Contrastive Learning and Extractive Argument Summarization
https://github.com/webis-de/naacl-24
공식 코드A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality
https://github.com/webis-de/ratio-24
공식 코드Are Large Language Models Reliable Argument Quality Annotators?
https://github.com/yanzhuguo/substanreview
공식 코드Automatic Analysis of Substantiation in Scientific Peer Reviews
https://github.com/yohanjo/emnlp20_prop_extr
공식 코드Extracting Implicitly Asserted Propositions in Argumentation
https://github.com/yohanjo/tacl_arg_rel
공식 코드Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.