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Open Information Extraction
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In natural language processing, open information extraction is the task of generating a structured, machine-readable representation of the information in text, usually in the form of triples or n-ary propositions (Source: Wikipedia).
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/urchade/filtered-semi-markov-crf
공식 코드https://github.com/urchade/hner
공식 코드Hierarchical Transformer Model for Scientific Named Entity Recognition
https://github.com/uw-bionlp/CACER
공식 코드CACER: Clinical Concept Annotations for Cancer Events and Relations
https://github.com/uyaseen/bionlp-ost-2019
공식 코드https://github.com/v-mipeng/LexiconAugmentedNER
공식 코드https://github.com/v-mipeng/LexiconNER
공식 코드Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning
https://github.com/v-mipeng/TaskOOV
공식 코드Learning Task-specific Representation for Novel Words in Sequence Labeling
https://github.com/viczong/extract_covid19_events_from_twitter
공식 코드COVID-19 event extraction from Twitter via extractive question answering with continuous prompts
https://github.com/vietnlp/etnlp
공식 코드ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task
https://github.com/vikas95/Pref_Suff_Span_NN
공식 코드A Survey on Recent Advances in Named Entity Recognition from Deep Learning models
https://github.com/viktorooreps/guided-bond
공식 코드https://github.com/vncorenlp/VnCoreNLP
공식 코드https://github.com/wangcongcong123/ttt
공식 코드https://github.com/wataruhashimoto52/ner_da_uncertainty
공식 코드Are Data Augmentation Methods in Named Entity Recognition Applicable for Uncertainty Estimation?
https://github.com/waysonren/pernee
공식 코드https://github.com/webspellchecker/unlp-2023-shared-task
공식 코드RedPenNet for Grammatical Error Correction: Outputs to Tokens, Attentions to Spans
https://github.com/wgantt/eks
공식 코드https://github.com/whoisjones/familarity
공식 코드https://github.com/wilburOne/ZeroShotEvent
공식 코드https://github.com/wjx-git/deptriggerner
공식 코드Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger
https://github.com/wonjininfo/CollaboNet
공식 코드CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
https://github.com/wzh-nlp/pltr
공식 코드Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features
https://github.com/wzh-nlp/www25-cmas
공식 코드A Cooperative Multi-Agent Framework for Zero-Shot Named Entity Recognition
https://github.com/xianjun-yang/fewdocae
공식 코드https://github.com/xiaoqian19940510/TASLP-EAREE
공식 코드Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations
https://github.com/xinyadu/doc_event_role
공식 코드Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding
https://github.com/xinyadu/eeqa
공식 코드https://github.com/xmb-cipher/fofe-ner
공식 코드A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection
https://github.com/xuuuluuu/SynLSTM-for-NER
공식 코드https://github.com/xuuuluuu/ds_ner
공식 코드Better Sampling of Negatives for Distantly Supervised Named Entity Recognition
https://github.com/yale-lily/r2d2
공식 코드https://github.com/yang233666/cmvc
공식 코드Multi-View Clustering for Open Knowledge Base Canonicalization
https://github.com/yanzhh/hgere
공식 코드Joint Entity and Relation Extraction with Span Pruning and Hypergraph Neural Networks
https://github.com/yhcc/BARTNER
공식 코드https://github.com/yhcc/cnn_nested_ner
공식 코드An Embarrassingly Easy but Strong Baseline for Nested Named Entity Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.