Solutions to Exercism exercises.

Using Stanford's CoreNLP, 'stanza', module to convert scraped reviews (TripAdvisor - Airlines) into CoNLL formatted .tsv files (for a sequence labelling task)

too many unstructured data, alok has too many req, seeing what helps

This project aims to implement word-based, character-based and subword-based tokenization techniques.

Code for sentence analysis and feature extraction using NLP

A post-processing pipeline to fact-check, entity-link, and verify answers from Large Language Models (LLMs). Developed for the Web Data Processing Systems course at Vrije Universiteit Amsterdam.

Measuring Slovak law's growing word count (and other metrics)

This project aims to implement the dependency-graph-convolutional-networks and dependency-graph-attention-networks for span detection from given text.

📱 This project implements an instant messaging client that supports the XMPP protocol. 💻

An extractor of keywords for Estonian texts.

This project aims to implement various word embedding methods.

Multi-model sentiment analysis REST API using NLTK, TextBlob, Flair & Stanford CoreNLP. Features 6 endpoints, Docker support, comprehensive tests, and 99% accuracy on social media data.

Graduation project aimed at composing multiple-choice questions for learning Ukrainian grammar. Best suited for A1 level of fluency in Ukrainian.

id-svo-extractor: Extract SVO triples from Indonesian text.

This project aims to implement the result of the sentence-level and word-level polarity of a given text.

This project is to match the offset of the pre-trained word embeddings such as BERT, ELMo, and Glove to the preprocessed token offset and its dependencies in various ways of preprocessing method such as Stanza and Spacy. Therefore, the result of this can be applied to the graph neural networks without concern of its mismatch of the preprocessed …

A comprehensive desktop application designed for intelligent text analysis and linguistic processing. Built with Python and tkinter, it provides advanced natural language processing capabilities including word frequency analysis, named entity recognition, pattern extraction, and multi-language support with automatic language detection.

Keyword identification with Stanza (Python NLP Package)

Some APIs around AI models, for NLP, with docker image.

Ainized Stanford NLP Python Library for Many Human Languages