Displaying 1 to 20 from 26 results

sling - SLING - A natural language frame semantics parser

  •    C++

SLING is a parser for annotating text with frame semantic annotations. It is trained on an annotated corpus using Tensorflow and Dragnn.The parser is a general transition-based frame semantic parser using bi-directional LSTMs for input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. It is a jointly trained model using only the text tokens as input and the transition system has been designed to output frame graphs directly without any intervening symbolic representation.

opencog - A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)

  •    Scheme

OpenCog is a framework for developing AI systems, especially appropriate for integrative multi-algorithm systems, and artificial general intelligence systems. Though much work remains to be done, it currently contains a functional core framework, and a number of cognitive agents at varying levels of completion, some already displaying interesting and useful functionalities alone and in combination. With the exception of MOSES and the CogServer, all of the above are in active development, are half-baked, poorly documented, mis-designed, subject to experimentation, and generally in need of love an attention. This is where experimentation and integration are taking place, and, like any laboratory, things are a bit fluid and chaotic.

ludwig - Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code

  •    Python

Ludwig is a toolbox built on top of TensorFlow that allows to train and test deep learning models without the need to write code. All you need to provide is a CSV file containing your data, a list of columns to use as inputs, and a list of columns to use as outputs, Ludwig will do the rest. Simple commands can be used to train models both locally and in a distributed way, and to use them to predict on new data.




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 .

Hackernews-NLU - Use Swift to interpret unstructured data from Hacker News

  •    Swift

Hackernews-NLU is a sample application that uses Watson Natural Language Understanding service to analyze the contents of trending news articles on Hackernews to give information about the concepts, entities, categories, keywords, sentiment, emotion etc. about the news article. Clicking on the button below creates a IBM Code DevOps Toolchain and deploys this application to IBM Code. The manifest.yml file [included in the repo] is parsed to obtain the name of the application, configuration details, and the list of services that should be provisioned. For further details on the structure of the manifest.yml file, see the Cloud Foundry documentation.


gsoc2018-3gm - 💫 Automated codification of Greek Legislation with NLP

  •    Python

Welcome to Government Gazette text mining, cross linking, and codification Project (or 3gm for short) using Natural Language Processing Methods and Practices on Greek Legislation. This project aims to provide with the most recent versions of each law, i.e. an automated codex via NLP methods and practices.

Arch-Data-Science - Archlinux PKGBUILDs for Data Science, Machine Learning, Deep Learning, NLP and Computer Vision

  •    Shell

Welcome to my repo to build Data Science, Machine Learning, Computer Vision, Natural language Processing and Deep Learning packages from source. My Data Science environment is running from a LXC container so Tensorflow build system, bazel, must be build with its auto-sandboxing disabled.

watson-second-opinion - Get a second opinion on Amazon products by analyzing product reviews with Watson Natural Language Understanding

  •    Javascript

This is the code pattern for https://2ndopinion.mybluemix.net/. In this Code Pattern, we will create a Node.js app that takes the reviews from an online shopping website, Amazon, and feeds them into the Watson Natural Language Understanding service. The reviews will be stored in a Cloudant database. The Watson Natural Language Understanding service will show the overall sentiments of the reviews. The sample application will do all the reading of reviews for you and will give an overall insight about them. The Code Pattern can be useful to developers that are looking into processing multiple documents with Watson Natural Language Understanding.

ConvAI-baseline - ConvAI baseline solution

  •    Python

Python packages will be installed by setup.sh script. Setup will download docker images, models and data files, so you have no need to download any of that by yourself.

deep-nlp-seminars - Materials for deep NLP course

  •    Jupyter

Also, please do not add your name to your homework, since we try to keep review process anonymous. Please, register your project here.

intent_classifier

  •    Python

Try it here. In this repo one can find code for training and infering intent classification that is presented as shallow-and-wide Convolutional Neural Network[1].

ner - Named Entity Recognition

  •    Python

In this repo you can find several neural network architectures for named entity recognition from the paper "Application of a Hybrid Bi-LSTM-CRF model to the task of Russian Named Entity Recognition" https://arxiv.org/pdf/1709.09686.pdf, which is inspired by LSTM+CRF architecture from https://arxiv.org/pdf/1603.01360.pdf. NER class from ner/network.py provides methods for construction, training and inference neural networks for Named Entity Recognition.

natural-language-understanding-nodejs - :new: Demo code for the Natural Language Understanding Service

  •    Javascript

Natural Language Understanding is a collection of APIs that offer text analysis through natural language processing. This set of APIs can analyze text to help you understand its concepts, entities, keywords, sentiment, and more. Additionally, you can create a custom model for some APIs to get specific results that are tailored to your domain. Open the .env file and add the service credentials that you obtained in the previous step.

text-bot-openwhisk - The Watson Weather Bot integrated with OpenWhisk.

  •    Javascript

This project gives you the current weather forecast for your city (U.S. only as of now). The Weather Bot uses Watson Assistant (formerly Conversation), Natural Language Understanding, and The Weather Company Data API. It is run with OpenWhisk. To deploy this application to IBM Cloud, click the Deploy to IBM Cloud button below.

snap-and-translate - Build a hybrid mobile app that can capture an image,recognize text and translate it using Tesseract OCR & Watson Language Translator

  •    Javascript

In this Code Pattern, we will create a hybrid mobile app using Apache Cordova and Node.js server application running on IBM Cloud Kubernetes service that uses Tesseract OCR to recognize text in images, Watson Language Translator to translate the recognized text and Watson Natural Language Understanding to extract emotion,sentiment from the text. This mobile app translates the recognized text from the images captured or uploaded from the photo album. This Code Pattern contains several pieces. The Node.js server application running on IBM Cloud Kubernetes service communicates with the Tesseract OCR, Watson Language Translator and Watson Natural Language Understanding. Mobile application is built locally and runs on the Android/iOS phone.