All Projects → jxwuyi → Atnre

jxwuyi / Atnre

Licence: bsd-3-clause
Adversarial Training for Neural Relation Extraction

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Atnre

adversarial-relation-classification
Unsupervised domain adaptation method for relation extraction
Stars: ✭ 18 (-83.33%)
Mutual labels:  nlp-machine-learning, relation-extraction
SENet-for-Weakly-Supervised-Relation-Extraction
No description or website provided.
Stars: ✭ 39 (-63.89%)
Mutual labels:  nlp-machine-learning, relation-extraction
Doc2vec
📓 Long(er) text representation and classification using Doc2Vec embeddings
Stars: ✭ 92 (-14.81%)
Mutual labels:  nlp-machine-learning
Repo 2016
R, Python and Mathematica Codes in Machine Learning, Deep Learning, Artificial Intelligence, NLP and Geolocation
Stars: ✭ 103 (-4.63%)
Mutual labels:  nlp-machine-learning
Ml Classifier
A tool for quickly training image classifiers in the browser
Stars: ✭ 97 (-10.19%)
Mutual labels:  tensorflow-experiments
Writeup Frontend
Beat Writer's Block with AI
Stars: ✭ 94 (-12.96%)
Mutual labels:  nlp-machine-learning
Zhopenie
Chinese Open Information Extraction (Tree-based Triple Relation Extraction Module)
Stars: ✭ 98 (-9.26%)
Mutual labels:  relation-extraction
Nlp
This is where I put all my work in Natural Language Processing
Stars: ✭ 90 (-16.67%)
Mutual labels:  tensorflow-experiments
Self Driving Car
Automated Driving in NFS using CNN.
Stars: ✭ 105 (-2.78%)
Mutual labels:  tensorflow-experiments
Copymtl
AAAI20 "CopyMTL: Copy Mechanism for Joint Extraction of Entities and Relations with Multi-Task Learning"
Stars: ✭ 97 (-10.19%)
Mutual labels:  relation-extraction
Mrc book
《机器阅读理解:算法与实践》代码
Stars: ✭ 102 (-5.56%)
Mutual labels:  nlp-machine-learning
Relation extraction
Relation Extraction using Deep learning(CNN)
Stars: ✭ 96 (-11.11%)
Mutual labels:  relation-extraction
Tre
[AKBC 19] Improving Relation Extraction by Pre-trained Language Representations
Stars: ✭ 95 (-12.04%)
Mutual labels:  relation-extraction
Question Generation
Given a sentence automatically generate reading comprehension style factual questions from that sentence, such that the sentence contains answers to those questions.
Stars: ✭ 100 (-7.41%)
Mutual labels:  nlp-machine-learning
Datascience
It consists of examples, assignments discussed in data science course taken at algorithmica.
Stars: ✭ 92 (-14.81%)
Mutual labels:  nlp-machine-learning
Textaugmentation Gpt2
Fine-tuned pre-trained GPT2 for custom topic specific text generation. Such system can be used for Text Augmentation.
Stars: ✭ 104 (-3.7%)
Mutual labels:  nlp-machine-learning
Lda Topic Modeling
A PureScript, browser-based implementation of LDA topic modeling.
Stars: ✭ 91 (-15.74%)
Mutual labels:  nlp-machine-learning
Monkeylearn
⛔️ ARCHIVED ⛔️ 🐒 R package for text analysis with Monkeylearn 🐒
Stars: ✭ 95 (-12.04%)
Mutual labels:  nlp-machine-learning
Intra Bag And Inter Bag Attentions
Code for NAACL 2019 paper: Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions
Stars: ✭ 98 (-9.26%)
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 (-2.78%)
Mutual labels:  relation-extraction

AtNRE: Adversarial training for Neural Relation Extraction

This repository is the source code for the paper:

Adversarial Training for Relation Extraction

Yi Wu, David Bamman, Stuart Russell

University of California, Berkeley

Conference on Empirical Methods in Natural Language Processing (EMNLP) 2017, Copenhagen, Denmark.

For data, please refer to the references in our paper and download from the original sources of the datasets.

Original NYT dataset (paper, link)

Original NAACL dataset (paper, link)

For reproducibility of our results, here is the processed pickled data used in the code. PLEASE (1) do not distributed and (2) refer to the original data sources for either personal use or academic purpose.

The code is under BSD-3 license.

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].