nlp-architect - NLP Architect by Intel AI Lab: Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding

•    Python

NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration. Framework documentation on NLP models, algorithms, and modules, and instructions on how to contribute can be found at our main documentation site.

snips-nlu - Snips Python library to extract meaning from text

•    Python

Snips NLU (Natural Language Understanding) is a Python library that allows to parse sentences written in natural language and extracts structured information. To find out how to use Snips NLU please refer to our documentation, it will provide you with a step-by-step guide on how to use and setup our library.

spark-nlp - Natural Language Understanding Library for Apache Spark.

•    Jupyter

John Snow Labs Spark-NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment. This library has been uploaded to the spark-packages repository https://spark-packages.org/package/JohnSnowLabs/spark-nlp .

TC-Bot - User Simulation for Task-Completion Dialogues

•    OpenEdge

An implementation of the End-to-End Task-Completion Neural Dialogue Systems and A User Simulator for Task-Completion Dialogues. This document describes how to run the simulation and different dialogue agents (rule-based, command line, reinforcement learning). More instructions to plug in your customized agents or user simulators are in the Recipe section of the paper.

awesome-hungarian-nlp - A curated list of NLP resources for Hungarian

A curated list of NLP resources for Hungarian

gdpr-fingerprint-pii - Use Watson Natural Language Understanding and Watson Knowledge Studio to fingerprint personal data from unstructured documents

•    Java

Read this in other languages: 한국어. General Data Protection Regulation (GDPR) will be a new regulation in EU which will come into effect in May 2018. This new regulation applies to those organizations, including those outside EU, which collect and process personal data. It aims to give more control to individuals over usage of their personal data.

watson-document-classifier - Augment IBM Watson Natural Language Understanding APIs with a configurable mechanism for text classification, uses IBM Data Science Experience (DSX)

•    Jupyter

Read this in other languages: 한국어. In this developer journey we will use Jupyter notebooks in IBM Data Science experience(DSX) to augment IBM Watson Natural Language Understanding API output through configurable mechanism for text classification.

watson-document-co-relation - Correlate text content across documents using Watson NLU, Python NLTK and IBM DSX

•    Jupyter

In this developer journey we will use Jupyter notebooks in IBM Data Science experience(DSX) to correlate text content across documents with Python NLTK toolkit and IBM Watson Natural Language Understanding. The correlation algorithm is driven by an input configuration json that contains the rules and grammar for building the relations. The configuration json document can be modified to obtain better correlation results between text content across documents. The intended audience for this journey is developers who want to learn a method for correlation of text content across documents. The distinguishing factor of this journey is that it allows a configurable mechanism of text correlation.

tracy - A simple and easy to use trainer to generate Rasa/Snips NLU datasets

•    Vue

A simple and easy to use trainer to generate Rasa NLU and Snips NLU needed files. Inspired by Chatito and Rasa NLU Trainer.

nlu_sim - all kinds of baseline models for sentence similarity

•    Python

all kinds of baseline models for modeling tasks with pair of sentences: semantic text similarity(STS), natural language inference(NLI), paraphrase identification(PI), question answering(QA). this repository contain models that learn to detect sentence similarity for natural language understanding tasks.

ZZZ-RETIRED_openstt - RETIRED - OpenSTT is now retired

RETIRED - OpenSTT is now retired. If you would like more information on Mycroft AI's open source STT projects, please visit:

graph-nlu - Graph NLU is a natural language understanding tool that leverages the power of graph databases

•    Jupyter

Graph NLU uses graph databases as a means to represent natural language relationships flexibly and dynamically. The primary motivation for this project is to develop a way to understand natural language dialog in an interactive setting by remembering previous dialog states. Virtual assistants like Siri, Google Assistant, and Alexa have the common problem that they behave like amnesiacs, i.e., they do not remember much about previous interactions.

snips-nlu-ontology - Ontology of the Snips NLU

•    Rust

Ontology of the Snips NLU library API which describes supported languages and builtin entities. Please refer to this page for the python wrapper. The following sections provide results examples for each builtin entity.

snips-nlu-rs - Snips NLU rust implementation

•    Rust

The purpose of the main crate of this repository, snips-nlu-lib, is to perform an information extraction task called intent parsing. In order to achieve such a result, the NLU engine needs to be fed with a trained model (json file). This repository only contains the inference part, in order to produce trained models please check the Snips NLU python library.

botpress-rasa_nlu - The Rasa NLU module for Botpress

•    Javascript

A module to use Rasa.ai with your Botpress bot. The Rasa NLU module should now be available in your dashboard.

deep-learning-nlp-rl-papers - Recent Deep Learning papers in NLU and RL

•    Python

I think that other people's notes are rarely useful, so I'm listing the interesting for me papers with a few words about the main idea for me to make references in memory. If you're in such stuff, welcome: papers list.

wit-go - Go client for wit.ai HTTP API

•    Go

This repository is community-maintained. We gladly accept pull requests. Please see the Wit HTTP Reference for all supported endpoints. Go client for wit.ai HTTP API.

nbayes - A Naive Bayes classifier written in JavaScript.

•    Javascript

nbayes is a lightweight Naive Bayes Classifier written in vanilla JavaScript. It classifies a document (arbitrary piece of text) among the classes (arbitrarily named categories) it has been trained with before. This is all based on simple mathematics. As an example, you could use nbayes to answer the following questions. nbayes offers a simple and straightforward API, keeping it below 3kb (minified). It is a rewrite of ttezel/bayes and thoroughly tested.

opsdroid - 🤖 An open source chat-ops bot framework

•    Python

An open source chat bot framework written in python. It is designed to be extendable, scalable and simple. This application is designed to take messages from chat services and execute python functions (skills) based on their contents. Those functions can be anything you like, from simple conversational responses to running complex tasks. The true power of this project is to act as a glue library to bring the multitude of natural language APIs, chat services and third party APIs together.

snips-nlu-metrics - Python package to compute metrics on an NLU intent parsing pipeline

•    Python

This tools is a python library for computing cross-validation and train/test metrics on an NLU parsing pipeline such as the Snips NLU one. Its purpose is to help evaluating and iterating on the tested intent parsing pipeline.