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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/lgessler/microbert
공식 코드https://github.com/liaad/tieval
공식 코드tieval: An Evaluation Framework for Temporal Information Extraction Systems
https://github.com/liatb282/metaxlr
공식 코드https://github.com/lighteternal/PENELOPIE
공식 코드PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation
https://github.com/lihaoxin2020/narrowbert
공식 코드NarrowBERT: Accelerating Masked Language Model Pretraining and Inference
https://github.com/limanling/clip-event
공식 코드CLIP-Event: Connecting Text and Images with Event Structures
https://github.com/liu-haoyue/ner-net
공식 코드https://github.com/liukun95/Noisy-NER-Confidence-Estimation
공식 코드https://github.com/liupeip-cs/iie4mner
공식 코드https://github.com/liyongqi2002/TadNER
공식 코드Type-Aware Decomposed Framework for Few-Shot Named Entity Recognition
https://github.com/liyp0095/CuPUL
공식 코드Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach
https://github.com/ljvmiranda921/calamancy
공식 코드https://github.com/ljynlp/w2ner
공식 코드Unified Named Entity Recognition as Word-Word Relation Classification
https://github.com/louchao98/nner_as_parsing
공식 코드Nested Named Entity Recognition as Latent Lexicalized Constituency Parsing
https://github.com/luka-group/pasta
공식 코드https://github.com/luyaojie/text2event
공식 코드Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction
https://github.com/lwl-cpu/deeia
공식 코드Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
https://github.com/lx865712528/ACL2019-ODEE
공식 코드Open Domain Event Extraction Using Neural Latent Variable Models
https://github.com/lx865712528/JMEE
공식 코드Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation
https://github.com/lxucs/multilingual-sl
공식 코드Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation
https://github.com/mainlp/auxtasks-bavarian-sid
공식 코드https://github.com/mainlp/barner
공식 코드Sebastian, Basti, Wastl?! Recognizing Named Entities in Bavarian Dialectal Data
https://github.com/mainlp/ner-disagreements
공식 코드Different Tastes of Entities: Investigating Human Label Variation in Named Entity Annotations
https://github.com/malllabiisc/AD3
공식 코드https://github.com/malllabiisc/cesi
공식 코드CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
https://github.com/malllabiisc/sictf
공식 코드Relation Schema Induction using Tensor Factorization with Side Information
https://github.com/markustyj/fsponer_ecai2024
공식 코드FsPONER: Few-shot Prompt Optimization for Named Entity Recognition in Domain-specific Scenarios
https://github.com/masakhane-io/masakhane-ner
공식 코드https://github.com/masakhane-io/masakhane-ner/tree/main/MasakhaNER2.0
공식 코드MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
https://github.com/masoud-monajati/llm_bio_ner
공식 코드LLMs in Biomedicine: A study on clinical Named Entity Recognition
https://github.com/matejklemen/morphological-comment-filtering
공식 코드Enhancing deep neural networks with morphological information
https://github.com/matejklemen/morphological-dependency-parsing
공식 코드Enhancing deep neural networks with morphological information
https://github.com/mckysse/gain
공식 코드https://github.com/mckysse/msd
공식 코드https://github.com/mebzmoren/cebuaner
공식 코드CebuaNER: A New Baseline Cebuano Named Entity Recognition Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.