The main objective of extractive summarization can be concisely formulated as extracting text inputs containing information on the most important concepts described in the input text or texts. I have tried to collect and curate some publications form Arxiv that related to the extractive summarization, and the results were listed here. Please enjoy it!
Information Extraction refers to the automatic extraction of structured information such as entities, relationships between entities, and attributes that describe entities from unstructured sources. I have tried to collect and curate some publications form bioRxiv that related to the information extraction in biology, such as relation extraction and feature extraction. The results were listed here. Please enjoy it!
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems, such as humans, learn in the presence of both labeled and unmarked data. I have tried to collect and curate some publications form Arxiv that related to the semi-supervised learning, and the results were listed here. Please enjoy it!
Sentiment Analysis ( SA) is a field of study that analyzes people’s feelings or opinions from reviews or opinions. Sentiment analysis can be seen as a natural language processing task, the task is to develop a system that understands people’s language. I have tried to collect and curate some Python-based Github repository linked to the sentiment analysis task, and the results were listed here. Please enjoy it to support your research about sentiment analysis using Python!
Computational biology, a biology branch involving the use of computers and computer science to understand and model the structures and processes of life. It involves the use of computational methods (e.g. algorithms) for the representation and simulation of biological systems, as well as for the interpretation of experimental data, often on a very large scale. I have tried to collect and curate some publications that related to the computational biology and bioinformatics using neural networks and the results were listed here. Please enjoy it!