738 Open source projects that are alternatives of or similar to Open Sesame

KoBERT와 CRF로 만든 한국어 개체명인식기 (BERT+CRF based Named Entity Recognition model for Korean)

🇺🇸 a python library for parsing unstructured address strings into address components

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.

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

基于深度学习的自然语言处理库

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

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

Chinese Biomedical Language Understanding Evaluation benchmark (ChineseBLUE)

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

中文自然语言的实体抽取和意图识别（Natural Language Understanding），可选Bi-LSTM + CRF 或者 IDCNN + CRF

Cantonese Linguistics and NLP in Python

g2pC: A Context-aware Grapheme-to-Phoneme Conversion module for Chinese

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

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

A news recommendation system tailored for user communities

A Library to parse natural language in pure Clojure and ClojureScript

Python library for Natural Language Preprocessing (NLPre)

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

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

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

SLING - A natural language frame semantics parser

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

Implementation of the paper Tree Transformer

📅🇨🇳 中国法定节假日数据 自动每日抓取国务院公告

A Ruby natural language processor.

Character-level Convolutional Neural Networks for text classification in PyTorch

Named Entity Recognition (LSTM + CRF) - Tensorflow

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

Python wrapper for evaluating summarization quality by ROUGE package

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.

Important paper implementations for Question Answering using PyTorch

Basic Utilities for PyTorch Natural Language Processing (NLP)

QANTA Quiz Bowl AI

Text vectorization tool to outperform TFIDF for classification tasks

Automatically extract chemical information from scientific documents

Mishkal is an arabic text vocalization software

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/

Paper list of NLP for recommender systems

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.

Topic Modelling for Humans

面向中文电子病历的命名实体识别

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

👩‍🏫 Advanced NLP with spaCy: A free online course

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

Ruby SDK for Dialogflow

An implementation of Conditional Random Fields (CRFs) with Deep Learning Method

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

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

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

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

A JavaFX based desktop search application.

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].

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

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.

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

阿里天池首届中文NL2SQL挑战赛top6

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

How to build RNNs and LSTMs from scratch with NumPy.

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

Data and software for building the ACL Anthology.