All Projects → dgai91 → Pytorch Acnn Model

dgai91 / Pytorch Acnn Model

code of Relation Classification via Multi-Level Attention CNNs

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Pytorch Acnn Model

Atnre
Adversarial Training for Neural Relation Extraction
Stars: ✭ 108 (-36.47%)
Mutual labels:  relation-extraction
Macadam
Macadam是一个以Tensorflow(Keras)和bert4keras为基础,专注于文本分类、序列标注和关系抽取的自然语言处理工具包。支持RANDOM、WORD2VEC、FASTTEXT、BERT、ALBERT、ROBERTA、NEZHA、XLNET、ELECTRA、GPT-2等EMBEDDING嵌入; 支持FineTune、FastText、TextCNN、CharCNN、BiRNN、RCNN、DCNN、CRNN、DeepMoji、SelfAttention、HAN、Capsule等文本分类算法; 支持CRF、Bi-LSTM-CRF、CNN-LSTM、DGCNN、Bi-LSTM-LAN、Lattice-LSTM-Batch、MRC等序列标注算法。
Stars: ✭ 149 (-12.35%)
Mutual labels:  relation-extraction
Sa Tensorflow
Soft attention mechanism for video caption generation
Stars: ✭ 154 (-9.41%)
Mutual labels:  attention-model
Linear Attention Recurrent Neural Network
A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)
Stars: ✭ 119 (-30%)
Mutual labels:  attention-model
Information Extraction Chinese
Chinese Named Entity Recognition with IDCNN/biLSTM+CRF, and Relation Extraction with biGRU+2ATT 中文实体识别与关系提取
Stars: ✭ 1,888 (+1010.59%)
Mutual labels:  relation-extraction
Open Ie Papers
Open Information Extraction (OpenIE) and Open Relation Extraction (ORE) papers and data.
Stars: ✭ 150 (-11.76%)
Mutual labels:  relation-extraction
Zhopenie
Chinese Open Information Extraction (Tree-based Triple Relation Extraction Module)
Stars: ✭ 98 (-42.35%)
Mutual labels:  relation-extraction
Jointnre
Joint Neural Relation Extraction with Text and KGs
Stars: ✭ 168 (-1.18%)
Mutual labels:  relation-extraction
Hatt Proto
Code and dataset of AAAI2019 paper Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
Stars: ✭ 149 (-12.35%)
Mutual labels:  relation-extraction
Deeplearning nlp
基于深度学习的自然语言处理库
Stars: ✭ 154 (-9.41%)
Mutual labels:  relation-extraction
Image Caption Generator
A neural network to generate captions for an image using CNN and RNN with BEAM Search.
Stars: ✭ 126 (-25.88%)
Mutual labels:  attention-model
Bertem
论文实现(ACL2019):《Matching the Blanks: Distributional Similarity for Relation Learning》
Stars: ✭ 146 (-14.12%)
Mutual labels:  relation-extraction
Tensorflow rlre
Reinforcement Learning for Relation Classification from Noisy Data(TensorFlow)
Stars: ✭ 150 (-11.76%)
Mutual labels:  relation-extraction
Bran
Full abstract relation extraction from biological texts with bi-affine relation attention networks
Stars: ✭ 111 (-34.71%)
Mutual labels:  relation-extraction
Fox
Federated Knowledge Extraction Framework
Stars: ✭ 155 (-8.82%)
Mutual labels:  relation-extraction
Pytorch multi head selection re
BERT + reproduce "Joint entity recognition and relation extraction as a multi-head selection problem" for Chinese and English IE
Stars: ✭ 105 (-38.24%)
Mutual labels:  relation-extraction
Kg Baseline Pytorch
2019百度的关系抽取比赛,使用Pytorch实现苏神的模型,F1在dev集可达到0.75,联合关系抽取,Joint Relation Extraction.
Stars: ✭ 149 (-12.35%)
Mutual labels:  relation-extraction
Relation Classification Using Bidirectional Lstm Tree
TensorFlow Implementation of the paper "End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures" and "Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths" for classifying relations
Stars: ✭ 167 (-1.76%)
Mutual labels:  relation-extraction
Ruijin round2
瑞金医院MMC人工智能辅助构建知识图谱大赛复赛
Stars: ✭ 159 (-6.47%)
Mutual labels:  relation-extraction
R Bert
Pytorch implementation of R-BERT: "Enriching Pre-trained Language Model with Entity Information for Relation Classification"
Stars: ✭ 150 (-11.76%)
Mutual labels:  relation-extraction

NRE ACNN Model

As you know the attention model can help us to solve many problems.Resently, I have a project which need to recognize the relation from some entities. After reading several paper, I decided to implement this paper: Relation Classification via Multi-Level Attention CNNs I desperately desire to use pytorch to do some awsome things. So it's the only choice for me. And i think you will like it.

Some of data handling codes are copied from ACNN

You need an environment: pytorch 1.0.0 keras & tensorflow (I only used one function which name is to_categorical) Git this project to your pycharm or other IDE, then edit the acnn_train.py to satisfied your data

18.12.17 The Renewed Version

These days, I reviewed the paper again and update my code. But the acc is still low.

Could somebody give me some advice?

Network Structure

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].