PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

Contextualised Embeddings and Language Modelling using BERT and Friends using R

Transformer Based SeqGAN for Language Generation

Code for my master thesis in Deep Learning: "Generating answers to medical questions using recurrent neural networks"

Pytorch implementation of MaskGAN

Language Modelling, CMI vs Perplexity

N-Gram language model that learns n-gram probabilities from a given corpus and generates new sentences from it based on the conditional probabilities from the generated words and phrases.

Spelling correction and grammar detection with statistical language models

This code project is associated with my master thesis (at the company Omicron Ceti AB) that I have done during spring 2019 at Royal Institute of Technology. The report, to which the code project is associated, is included in this Github repository as "report.pdf".

Introduce basic nlp tasks and methods with examples

Reproduction of CIFAR-10/CIFAR-100 and Penn Treebank experiments to test claims in "LookaheadOptimizer: k steps forward, 1 step back" https://arxiv.org/abs/1907.08610

Reproduction of CIFAR-10/CIFAR-100 and Penn Treebank experiments to test claims in "LookaheadOptimizer: k steps forward, 1 step back" https://arxiv.org/abs/1907.08610

Distributed tensorflow port for character rnn

In this project we will generate the sentences using ngrams

A simple series of programs to train gated recurrent neural networks with PyTorch and generate text based on them.

An Implementation One of Natural Language Processing Method : Language Modelling