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

fastNLP: A Modularized and Extensible NLP Framework. Currently still in incubation.

Sentiment analysis on Amazon Review Dataset available at http://snap.stanford.edu/data/web-Amazon.html

Global-Locally Self-Attentive Dialogue State Tracker

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Mishkal is an arabic text vocalization software

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Topic Modelling for Humans

A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.

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借助Python抓取微博数据，并对抓取的数据进行情绪分析

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].

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

How to build RNNs and LSTMs from scratch with NumPy.

基于深度学习的自然语言处理库

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

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

NLP Tensorflow Tutorials

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

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

A Library to parse natural language in pure Clojure and ClojureScript

A privacy preserving NLP framework

Chinese Biomedical Language Understanding Evaluation benchmark (ChineseBLUE)

a sklearn wrapper for Google's BERT model

💙 Emoji handling and meta data for spaCy with custom extension attributes

Vietnamese NLP Toolkit for Node

A natural language date parser in Swift (ported from chrono.js)

IssuesにNLP（自然言語処理）に関連するの論文を読んだまとめを書いています．雑です．🚧 マークは編集中の論文です（事実上放置のものも多いです）．🍡 マークは概要のみ書いてます（早く見れる的な意味で団子）．

Collection of stats, modeling, and data science tools in Python and R.

Cantonese Linguistics and NLP in Python

Japanese negative positive classification.日本語文書のネガポジを判定。

Implementation of the paper Tree Transformer

A dialogue bot for information access

Simple State-of-the-Art BERT-Based Sentence Classification with Keras / TensorFlow 2. Built with HuggingFace's Transformers.

Türkiye'de yapılan derin öğrenme (deep learning) ve makine öğrenmesi (machine learning) çalışmalarının derlendiği sayfa.

A Ruby natural language processor.

Data and software for building the ACL Anthology.

A JavaFX based desktop search application.

DGMs for NLP. A roadmap.

top 1% solution to toxic comment classification challenge on Kaggle.

Sentiment Classification using Word Sense Disambiguation

This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.

data resource untuk NLP bahasa indonesia

Text vectorization tool to outperform TFIDF for classification tasks

阿里天池首届中文NL2SQL挑战赛top6

A simple NLP library allows profiling datasets with one or more text columns. When given a dataset and a column name containing text data, NLP Profiler will return either high-level insights or low-level/granular statistical information about the text in that column.

⚡️A Lightweight NLP Data Loader for All Deep Learning Frameworks in Python

Machine learning models to automatically summarise scientific papers

互联网大厂面试经验

It is a question-generator model. It takes text and an answer as input and outputs a question.

This repository contains landmark research papers in Natural Language Processing that came out in this century.

Word Embeddings for Information Retrieval

NeuralQA: A Usable Library for Question Answering on Large Datasets with BERT

Default English stopword lists from many different sources

🐍 Turkish Language Stemmer for Python

Implementation of Siamese Neural Networks built upon multihead attention mechanism for text semantic similarity task.

Official Python client for the MonkeyLearn API. Build and consume machine learning models for language processing from your Python apps.

Amacımız Türkçe NLP literatüründeki birçok farklı sorunu bir arada çözebilen, eşsiz yaklaşımlar öne süren ve literatürdeki çalışmaların eksiklerini gideren open source bir yazım destekleyicisi/denetleyicisi oluşturmak. Kullanıcıların yazdıkları metinlerdeki yazım yanlışlarını derin öğrenme yaklaşımıyla çözüp aynı zamanda metinlerde anlamsal analizi de gerçekleştirerek bu bağlamda ortaya çıkan yanlışları da fark edip düzeltebilmek.

Code for the ACL 2018 paper "Neural Document Summarization by Jointly Learning to Score and Select Sentences"