926 Open source projects that are alternatives of or similar to Dostoevsky

TensorFlow implementation of Hierarchical Attention Networks for Document Classification and some extension

Multi-Task Deep Neural Networks for Natural Language Understanding

借助Python抓取微博数据，并对抓取的数据进行情绪分析

Curated research at the intersection of causal inference and natural language processing.

A suite of Arabic natural language processing tools developed by the CAMeL Lab at New York University Abu Dhabi.

🇺🇸 a python library for parsing unstructured address strings into address components

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

Getting started with AllenNLP and PyTorch by training a tweet classifier

Machine Learning University: Accelerated Natural Language Processing Class

Cornell Touchdown natural language navigation and spatial reasoning dataset.

Aspect Based Sentiment Analysis using End-to-End Memory Networks

A sentiment and emotion analysis server in Python

A baseline implementation for FNC-1

CapsNet for NLP

How to build RNNs and LSTMs from scratch with NumPy.

Machine reading comprehension on clinical case reports

A Python package for gender classification.

A comparison tool of Japanese tokenizers

Deep-Atrous-CNN-Text-Network: End-to-end word level model for sentiment analysis and other text classifications

Calculating ROUGE score between two files (line-by-line)

PyTorch Implementation of OpenAI GPT-2

基于深度学习的自然语言处理库

A two-level morphological analyzer for Turkish.

My completed implementation solutions for CS224N 2019

R package for Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing Based on the UDPipe Natural Language Processing Toolkit

Helsinki Prosody Corpus and A System for Predicting Prosodic Prominence from Text

This script can tell you the sentiments of people regarding to any events happening in the world by analyzing tweets related to that event

A TensorFlow implementation for "Recurrent Attention Network on Memory for Aspect Sentiment Analysis"

Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/

Repository for NLI models (EMNLP 2018)

DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue

Sentiment Analysis on the First Republic Party debate in 2016 based on Python,NLTK and ML.

This repo contains my coursework, assignments, and Slides for Natural Language Processing Specialization by deeplearning.ai on Coursera

ConText v4: Neural networks for text categorization

NLP Tensorflow Tutorials

The Open Source AI Chatbot Platform Builder in 100% C# Running in .NET Core with Machine Learning algorithm.

Code for paper "Discourse-Aware Neural Extractive Text Summarization" (ACL20)

Multi-layer Recurrent Neural Networks for character-level language models implements by TensorFlow

Examine two sentences and determine whether they have the same meaning.

A Neural Framework for MT Evaluation

Python implementation of TextRank for phrase extraction and summarization of text documents

Transition-based joint syntactic dependency parser and semantic role labeler using a stack LSTM RNN architecture.

Builds wordpiece(subword) vocabulary compatible for Google Research's BERT

Deep Recurrent Generative Decoder for Abstractive Text Summarization in DyNet

Dynamic Coattention Network Plus (DCN+) TensorFlow implementation. Question answering using Deep NLP.

🌊HMTL: Hierarchical Multi-Task Learning - A State-of-the-Art neural network model for several NLP tasks based on PyTorch and AllenNLP

A Library to parse natural language in pure Clojure and ClojureScript

Some JavaScript works published as demos, mostly ML or DS

A very simple framework for state-of-the-art Natural Language Processing (NLP)

Corpus of Annual Reports in Japan

A privacy preserving NLP framework

1st Place Solution for CrowdFlower Product Search Results Relevance Competition on Kaggle.

Multitask Deep Learning for Sentiment Analysis using Character-Level Language Model, Bi-LSTMs for POS Tag, Chunking and Unsupervised Dependency Parsing. Inspired by this great article https://arxiv.org/abs/1611.01587

Let your talking do the code

Deep Learning based Automatic Speech Recognition with attention for the Nvidia Jetson.

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

📓 Long(er) text representation and classification using Doc2Vec embeddings

🏖TagEditor - Annotation tool for spaCy