Papers with Code 아카이브
Semantic Role Labeling
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- 하위 태스크
- 3
- 평가표
- 7
- 보존된 평가 기록
- 40
Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". BIO notation is typically used for semantic role labeling.
Example:
| Housing | starts | are | expected | to | quicken | a | bit | from | August’s | pace |
|---|---|---|---|---|---|---|---|---|---|---|
| B-ARG1 | I-ARG1 | O | O | O | V | B-ARG2 | I-ARG2 | B-ARG3 | I-ARG3 | I-ARG3 |
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DalstonChen/CapNetSRL
공식 코드Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks
https://github.com/DevSinghSachan/syntax-augmented-bert
공식 코드Do Syntax Trees Help Pre-trained Transformers Extract Information?
https://github.com/Frozenmad/AMN_SRL
공식 코드https://github.com/GaryYufei/bestParseSRL
공식 코드https://github.com/HIT-SCIR/ltp
공식 코드N-LTP: An Open-source Neural Language Technology Platform for Chinese
https://github.com/HLR/SRLGRN
공식 코드https://github.com/Heidelberg-NLP/SRL-S2S
공식 코드Translate and Label! An Encoder-Decoder Approach for Cross-lingual Semantic Role Labeling
https://github.com/Heidelberg-NLP/xsrl_mbert_aligner
공식 코드X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset
https://github.com/HornHehhf/TAWT
공식 코드https://github.com/Impavidity/relogic
공식 코드Simple BERT Models for Relation Extraction and Semantic Role Labeling
https://github.com/JiaxunCai/Dynet-Biaffine-SRL
공식 코드A Full End-to-End Semantic Role Labeler, Syntax-agnostic Over Syntax-aware?
https://github.com/KiroSummer/A_Syntax-aware_MTL_Framework_for_Chinese_SRL
공식 코드A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling
https://github.com/KiroSummer/Syntax-aware-Neural-SRL
공식 코드https://github.com/LCS2-IIITD/LUMEN-Explaining-Memes
공식 코드What do you MEME? Generating Explanations for Visual Semantic Role Labelling in Memes
https://github.com/LUNAProject22/CLIPSitu
공식 코드Effectively Leveraging CLIP for Generating Situational Summaries of Images and Videos
https://github.com/ShannonAI/glyce
공식 코드https://github.com/Stareru/NeighboringDistributionDivergence
공식 코드Contextualized Semantic Distance between Highly Overlapped Texts
https://github.com/TheShadow29/VidSitu
공식 코드https://github.com/UKPLab/linspector
공식 코드LINSPECTOR: Multilingual Probing Tasks for Word Representations
https://github.com/X-F-Cui/FrameNet-Conditional-Generation
공식 코드Annotating FrameNet via Structure-Conditioned Language Generation
https://github.com/XMUNLP/Tagger
공식 코드https://github.com/allenai/allennlp
공식 코드AllenNLP: A Deep Semantic Natural Language Processing Platform
https://github.com/amazon-research/tanl
공식 코드Structured Prediction as Translation between Augmented Natural Languages
https://github.com/ant-research/structuredlm_rtdt
공식 코드Augmenting Transformers with Recursively Composed Multi-grained Representations
https://github.com/asofiaoliveira/srl_bert_pt
공식 코드Improving Portuguese Semantic Role Labeling with Transformers and Transfer Learning
https://github.com/ayush-pancholy/sister-help
공식 코드Sister Help: Data Augmentation for Frame-Semantic Role Labeling
https://github.com/bcmi220/hosrl
공식 코드https://github.com/bcmi220/multilingual_srl
공식 코드https://github.com/bgunlp/hebrew_srl
공식 코드Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles
https://github.com/bloomberg/emnlp20_depsrl
공식 코드https://github.com/cgraywang/deepstruct
공식 코드DeepStruct: Pretraining of Language Models for Structure Prediction
https://github.com/ch4osmy7h/framenetparser
공식 코드A Graph-Based Neural Model for End-to-End Frame Semantic Parsing
https://github.com/chenllliang/atp
공식 코드ATP: AMRize Then Parse! Enhancing AMR Parsing with PseudoAMRs
https://github.com/cooelf/SemBERT
공식 코드https://github.com/cytai/srlood
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.