pyKCN: A Python Tool for Bridging Scientific Knowledge
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Updated
May 30, 2024 - Python
pyKCN: A Python Tool for Bridging Scientific Knowledge
NLP toolkit for those nonsensical ontologies
A full-text article retrieval pipeline for biomedical literature.
Projects I have worked during my Bachelor
Palladian is a Java-based toolkit with functionality for text processing, classification, information extraction, and data retrieval from the Web.
Python & command-line tool to gather text on the Web: web crawling/scraping, extraction of text, metadata, comments
Scripts and database design that were used to analyse a large group of archaeological reports to search for....
A Locality Sensitive Hashing (LSH) library with an emphasis on large, highly-dimensional datasets.
We gather Malaysian dataset! https://malaysian-dataset.readthedocs.io/
Data and scripts for training the open source PDF questionnaire extraction component for Harmony Kaggle competition using natural language processing (NLP)
Extract text from papers PDFs and abstracts, and remove uninformative words.
Erlaubt anderen Programmen/Programmiersprachen den Zugriff auf Analysen/Daten des CorpusExplorer v2.0
Project in the course TDDE16 - Text Mining at Linköping University
Extension of the SentenceSimplification project
frances is an advanced cloud-based text mining digital platform that leverages information extraction, knowledge graphs, natural language processing (NLP), deep learning, and parallel processing techniques. It has been specifically designed to unlock the full potential of historical digital textual collections.
🧫 A curated list of resources relevant to doing Biomedical Information Extraction (including BioNLP)
Mono Repository for GengoAI projects
This project aims to build a model to predict the truth of an article, hoax or non-hoax. Apart from that, this project also wants to identify the percentage of hoax and non-hoax articles.
We unified some latent block models by proposing a flexible ELBM that is extended to SELBM to address the sparse problem by revealing a diagonal structure from sparse datasets. This leads to obtain more homogeneous co-clusters and therefore produce useful, ready-to-use and easy-to-interpret results.
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