Papers with Code 아카이브
Coreference Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 154
Coreference resolution is the task of clustering mentions in text that refer to the same underlying real world entities.
Example:
+-----------+
| |
I voted for Obama because he was most aligned with my values", she said.
| | |
+-------------------------------------------------+------------+
"I", "my", and "she" belong to the same cluster and "Obama" and "he" belong to the same cluster.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sooftware/nlp-attentions
https://github.com/soujanyarbhat/aNswER_multirc
https://github.com/soumik12345/transformer.pytorch
https://github.com/soutsios/pos-tagger-bert-tensorflow
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/souvikshanku/translit-former
https://github.com/stefan-it/europeana-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/stefan-it/historic-ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/stevinc/Transformer_Timeseries
https://github.com/studio-ousia/luke
https://github.com/sunyilgdx/prompts4keras
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/surancy/nlp-classification-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/svakulenk0/response_eval
https://github.com/swarnahub/explanationhardness
WinoGrande: An Adversarial Winograd Schema Challenge at Scale
https://github.com/swimmingCreative/DeepSpeechRecognition-master
https://github.com/sydney-machine-learning/sentimentanalysis-uselections
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tanjeffreyz/attention-is-all-you-need
https://github.com/tatp22/linformer-pytorch
https://github.com/tatp22/multidim-positional-encoding
https://github.com/tbmoon/LANL_Earthquake_Prediction
https://github.com/tcnguyen/bert
https://github.com/tcxdgit/MachineTranslation_t2t
https://github.com/tdd-ai/data-collection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/teghub/TurkishNER-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tensorflow/models/tree/master/official/nlp
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tensorflow/models/tree/master/research/lm_commonsense
https://github.com/tensorops/TransformerX
https://github.com/text-machine-lab/transformerpy
https://github.com/textflint/textflint
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
https://github.com/thanhlong1997/bert_quora
https://github.com/the-Quert/iNLPfun
https://github.com/theQuert/inlpfun
https://github.com/thecodemasterk/BERT
https://github.com/thecodemasterk/Text-to-Text-transfer-transformers
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/theneuralbeing/bert-finetuning-webinar
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/theoremone/llm-benchmarker-suite
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.