Papers with Code 아카이브

Event Extraction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 3
평가표: 9
보존된 평가 기록: 13

Determine the extent of the events in a text.

Other names: Event Tagging; Event Identification

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme

Deep Active Learning for Named Entity Recognition

https://github.com/twitter-research/multilingual-alignment-tpp

Improved Multilingual Language Model Pretraining for Social Media Text via Translation Pair Prediction

https://github.com/uds-lsv/Training-a-Neural-Network-in-a-Low-Resource-Setting-on-Automatically-Annotated-Noisy-Data

Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

https://github.com/ufal/acl2019_nested_ner

Neural Architectures for Nested NER through Linearization

https://github.com/uloveqian2021/NER

CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese

https://github.com/universal-ner/universal-ner

UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition

https://github.com/vedantc6/mtl-dts

Deeper Task-Specificity Improves Joint Entity and Relation Extraction

https://github.com/vincenzo-scotti/def_2_vec

https://github.com/vinhdv1628/etnlp

ETNLP: a visual-aided systematic approach to select pre-trained embeddings for a downstream task

https://github.com/voidful/NLPrep

A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text

https://github.com/wasiahmad/GATE

GATE: Graph Attention Transformer Encoder for Cross-lingual Relation and Event Extraction

https://github.com/xinyadu/rgqa

Event Extraction by Answering (Almost) Natural Questions

https://github.com/yangyucheng000/bert_Ascend

NEZHA: Neural Contextualized Representation for Chinese Language Understanding

https://github.com/ye-kyaw-thu/preprints

myNER: Contextualized Burmese Named Entity Recognition with Bidirectional LSTM and fastText Embeddings via Joint Training with POS Tagging

https://github.com/yuchenlin/CDMA-NER

Neural Adaptation Layers for Cross-domain Named Entity Recognition

https://github.com/yuwfan/FILTER

FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding

https://github.com/zhangyuwangumass/Glyph-based-Chinese-Character-Embedding

Glyce: Glyph-vectors for Chinese Character Representations

https://github.com/zjuym/chinese_cws_ner

Fast and Accurate Entity Recognition with Iterated Dilated Convolutions

https://github.com/zysite/post

Empower Sequence Labeling with Task-Aware Neural Language Model

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.