712 Open source projects that are alternatives of or similar to Kb Infobot

This repo contains implementation of different architectures for emotion recognition in conversations.

Paper list of NLP for recommender systems

A comprehensive reading list for Emotion Recognition in Conversations

Paper reading list in natural language processing, including dialogue systems and text generation related topics.

Source code for end-to-end dialogue model from the MultiWOZ paper (Budzianowski et al. 2018, EMNLP)

NIPS Conversational Intelligence Challenge 2017 Winner System: Skill-based Conversational Agent with Supervised Dialog Manager

ConvAI baseline solution

Global-Locally Self-Attentive Dialogue State Tracker

A collection of research on knowledge graphs

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）．

NNDial is an open source toolkit for building end-to-end trainable task-oriented dialogue models. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.

Attention-based multimodal fusion for sentiment analysis

Conversational AI Reading Materials

RNNLG is an open source benchmark toolkit for Natural Language Generation (NLG) in spoken dialogue system application domains. It is released by Tsung-Hsien (Shawn) Wen from Cambridge Dialogue Systems Group under Apache License 2.0.

This repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study

PyTorch implementation of latent space reinforcement learning for E2E dialog published at NAACL 2019. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU

Library to scrape and clean web pages to create massive datasets.

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

Browse Covid-19 & SARS-CoV-2 Scientific Papers with Transformers 🦠 📖

A Curated List of Dataset and Usable Library Resources for NLP in Bahasa Indonesia

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

❤️Emotional First Aid Dataset, 心理咨询问答、聊天机器人语料库

Basic Utilities for PyTorch Natural Language Processing (NLP)

Mishkal is an arabic text vocalization software

A frame-semantic parsing system based on a softmax-margin SegRNN.

Topic Modelling for Humans

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

State of the Art Natural Language Processing

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

Entity linking framework

This is a JSON driven angular x based dashboard framework that is inspired by JIRA's dashboard implementation and https://github.com/raulgomis/angular-dashboard-framework

A privacy preserving NLP framework

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

Statistical NLG for spoken dialogue systems

《机器翻译：基础与模型》肖桐 朱靖波 著 - Machine Translation: Foundations and Models

Vietnamese NLP Toolkit for Node

Python library for Natural Language Preprocessing (NLPre)

R package providing annotators and a normalized data model for natural language processing

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

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

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.

SLING - A natural language frame semantics parser

Cookiecutter API for creating Custom Skills for Azure Search using Python and Docker

Web scrapping and related analytics using Python tools

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

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

📅🇨🇳 中国法定节假日数据 自动每日抓取国务院公告

Data and software for building the ACL Anthology.

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].

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

Julia Implementation of Transformer models

Text vectorization tool to outperform TFIDF for classification tasks

How to build RNNs and LSTMs from scratch with NumPy.

Python wrapper for evaluating summarization quality by ROUGE package

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

基于深度学习的自然语言处理库

Important paper implementations for Question Answering using PyTorch

My completed implementation solutions for CS224N 2019

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

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