Natural language processing (NLP) is a field of computer science that studies how computers and humans interact. In the 1950s, Alan Turing published an article that proposed a measure of intelligence, now called the Turing test. More modern techniques, such as deep learning, have produced results in the fields of language modeling, parsing, and natural-language tasks.
A Hierarchical Type system for fine grained entity typing
MachineLearningSamples-BiomedicalEntityExtraction
Contains various architectures and novel paper implementations for Natural Language Processing tasks like Sequence Modelling and Neural Machine Transl...
Official PHP client for the MonkeyLearn API. Build and consume machine learning models for language processing from your PHP apps.
An open relation extraction system
Character-level CNN for text classification
🔠 Evolution of Language and Information Technology
Vietnamese tokenizer (Maximum Matching and CRF)
Intro to Natural Language Processing Tutorial
(Python) transform a document-term matrix to an Okapi/BM25 representation
Code for the old version of BioMedICUS, for the new version see the biomedicus3 repository.
Stanford cs224n. 斯坦福2019最新cs224n学习资料 个人学习笔记和解读以及作业解答。 http://web.stanford.edu/class/cs224n/
TextGraphs-13 Shared Task on Multi-Hop Inference Explanation Regeneration
Code for PyCon JP 2019 talk "Python による日本語自然言語処理 〜系列ラベリングによる実世界テキスト分析〜"
Implementation of "Episodic Memory in Lifelong Language Learning"(NeurIPS 2019) in Pytorch
NLP Specialization (Natural Language Processing) made by deeplearning.ai
CrowdTruth framework for crowdsourcing ground truth for training & evaluation of AI systems
Implementation of the EMNLP 2020 paper "Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition".
Word2Vec model trained across 640k+ materials science journal articles
IronMan's Jarvis with python
Question-answer generation from text
Tensorflow Implementation of Relation Networks for the bAbI QA Task, detailed in "A Simple Neural Network Module for Relational Reasoning," [https://a...
A Python3 wrapper tool to help using ITU Turkish NLP Pipeline API -- UNMAINTAINED --
Code for the ACL 2019 paper ``A Hierarchical Reinforced Sequence Operation Method for Unsupervised Text Style Transfer``
Few- and Zero-shot Multi-Label Learning for Structured Label Spaces
A simple tokenizer in Ruby for NLP tasks.
Code and Data for all experiments from our ACL 2018 paper "Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking"
Large scale sentential paraphrases collection and annotation
Açık Seminer (https://www.acikseminer.com/) serisinin doğal dil işleme haftasındaki 14. günündeki NLP 101: Doğal Dil İşlemeye Giriş ve 20. günündeki D...
Progressively Pretrained Dense Corpus Index for Open-Domain QA and Information Retrieval
A Natural Language Toolkit for Indian Languages
(arXiv:1509.06664) Reasoning about Entailment with Neural Attention.
The repo contains all the materials related to Question Answering.
✍️ Convolutional Recurrent Neural Network in Pytorch | Text Recognition
Public release of data and code for materials synthesis generation
Go package to convert natural language strings to numbers
Pipeline for training NER models using PyTorch.
Training a neural network to isolate comprehensible input from unstructured text to aid vocabulary acquisition.
Augment IBM Watson Natural Language Understanding APIs with a configurable mechanism for text classification, uses Watson Studio.
TensorFlow implementation of 'Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (2015)'
A curated list of natural language processing courses, video lectures, books, library and many more.
Visualization tools to use with RBERT
Text Mining certain fields from a resume
Experiments on Multilingual NMT
Natural Language Processing of Chicago news articles
Fine-grained Sentiment Classification Using BERT
Extracts key terminology (n-grams) from any large collection of documents (>1000) and forecasts emergence
Named-Entity Recognition in Persian Language
Blocks World -- Simulator, Code, and Models (Misra et al. EMNLP 2017)