758 Open source projects that are alternatives of or similar to Nlpre

THE String Processing Package for R (with ICU)

Text vectorization tool to outperform TFIDF for classification tasks

🌿 An easy-to-use Japanese Text Processing tool, which makes it possible to switch tokenizers with small changes of code.

Mycroft's multilingual text parsing and formatting library

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

Stanford NLP group's shared Python tools.

Open Korean Text Processor - An Open-source Korean Text Processor

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

CogComp's light-weight Python NLP annotators

Preprocessing Library for Natural Language Processing

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.

A JavaFX based desktop search application.

Automatically extract chemical information from scientific documents

Repository for paper "SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference"

R client for the Google Translation API, Google Cloud Natural Language API and Google Cloud Speech API

The CRF Layer was implemented by using Chainer 2.0. Please see more details here: https://createmomo.github.io/2017/09/12/CRF_Layer_on_the_Top_of_BiLSTM_1/

互联网大厂面试经验

Worth-reading papers and related awesome resources on aspect-based sentiment analysis (ABSA). 值得一读的方面级情感分析论文与相关资源集合

NLP progress in Fintech. A repository to track the progress in Natural Language Processing (NLP) related to the domain of Finance, including the datasets, papers, and current state-of-the-art results for the most popular tasks.

A web app to create and browse text visualizations for automated customer listening.

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

Pure-Python Japanese character interconverter for Hiragana, Katakana, Hankaku and Zenkaku

Python wrapper for evaluating summarization quality by ROUGE package

Util collection for Japanese text processing. Hiraganize, Katakanize, and Romanize.

Pre-Trained Models for ToD-BERT

Data augmentation for NLP

A Ruby natural language processor.

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

Character-level Convolutional Neural Networks for text classification in PyTorch

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].

阿里天池首届中文NL2SQL挑战赛top6

A Library to parse natural language in pure Clojure and ClojureScript

Machine learning models to automatically summarise scientific papers

SLING - A natural language frame semantics parser

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

Chinese Biomedical Language Understanding Evaluation benchmark (ChineseBLUE)

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

How to build RNNs and LSTMs from scratch with NumPy.

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

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

📖 A curated list of resources dedicated to Natural Language Processing (NLP)

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

LexPredict ContraxSuite

📚Survey of previous research and related works on machine learning (especially Deep Learning) in Japanese

👩‍🏫 Advanced NLP with spaCy: A free online course

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

基于深度学习的自然语言处理库

Code for Learning to select data for transfer learning with Bayesian Optimization

Ruby SDK for Dialogflow

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

PyTorch code for Learning Cooperative Visual Dialog Agents using Deep Reinforcement Learning

Important paper implementations for Question Answering using PyTorch

Cantonese Linguistics and NLP in Python

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

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.

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

Code for Sluice networks: Learning what to share between loosely related tasks

💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

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