中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

Named Entity Recognition (LSTM + CRF) - Tensorflow

A PyTorch Tutorials of Sentiment Analysis Classification (RNN, LSTM, Bi-LSTM, LSTM+Attention, CNN)

中文自然语言的实体抽取和意图识别（Natural Language Understanding），可选Bi-LSTM + CRF 或者 IDCNN + CRF

文本分类， 双向lstm + attention 算法

2BiVQA is a no-reference deep learning based video quality assessment metric.

See http://github.com/onurgu/joint-ner-and-md-tagger This repository is basically a Bi-LSTM based sequence tagger in both Tensorflow and Dynet which can utilize several sources of information about each word unit like word embeddings, character based embeddings and morphological tags from an FST to obtain the representation for that specific wor…

Keras implementation of path-based link prediction model for knowledge graph completion

handwritten word recognition with IAM dataset using CNN-Bi-LSTM and Bi-GRU implementation.

Official repo of the article: Yousef, W. A., Ibrahime, O. M., Madbouly, T. M., & Mahmoud, M. A. (2019), "Learning meters of arabic and english poems with recurrent neural networks: a step forward for language understanding and synthesis", arXiv preprint arXiv:1905.05700

Key Information Extraction from Scanned Receipts: The aim of this project is to extract texts of a number of key fields from given receipts, and save the texts for each receipt image in a JSON file.

Named Entity Recognition with Bidirectional LSTM-CNNs

Sleep stage classification using BCG-based pressure signals and residual-biLSTM networks

Phrase Extraction using Bi Directional LSTM

doctor_prescription_recognization_using_DeepLearning project for epics

Students Engagement Detection Using Hybrid EfficientNetB7 Together With TCN, LSTM, and Bi-LSTM (DAiSEE and VRESEE datasets)

Implementation of sentence comparison model using inner attention

Automated Sleep Stage Scoring using Deep Learing

Authorship Identification for Reuters C50-50 dataset. Comparision between LSTM, GRU, Bi-LSTM models and also highlights use of 50 different stylometry features for classification.