Educational Text Mining is a rapidly growing field, thanks to the adoption of modern probabilistic and machine learning algorithms. The current study focuses on student e-assessment by means of open-ended questions that require free-text answers (i.e., student essays), whose analysis and evaluatio...
In the realm of text mining, two significant scientific challenges revolve around document clustering and topic modeling, both aimed at extracting valuable insights from large collections of text. Topic modeling techniques were developed to automatically uncover latent themes in the documents, enabling mor...
Python Charmve/PaperWeeklyAI Sponsor Star78 Code Issues Pull requests 📚「@MaiweiAI」Studying papers in the fields of computer vision, NLP, and machine learning algorithms every week. nlpdata-sciencemachine-learningdata-miningcomputer-visiondeep-learningadvancedmachine-learning-algorithmstutorialspapersappl...
What is the difference between data mining, statistics, machine learning and AI? Would it be accurate to say that they are 4 fields attempting to solve very similar problems but with different approaches? What exactly do they have in common and where do they differ? If there is some kind ...
GitHub topics are specified (or even created) by users to classify their repositories. However, this manual process results in inaccurate labeling in some situations. For instance, a user can define bothpythonandpython-3as topics for its repository, which are redundant. Refining the list of topic...
The wrapper script is invoked in one of two ways, either by a short piece of Python code, the text of which is embedded in the Swift/T script and executed directly by the Swift/T runtime embedded Python inter- preter, or by a bash script that is executed via a Swift/T app function...
SNAPFootnote14: Stanford Network Analysis Platform is a general purpose network analysis and graph mining library. The core is written in C++ and there is also a python version built as a wrapper around the C++ one. The project is active, provides two crisp algorithms (the Girvan-Newman and ...
This paper aims to summarize the current knowledge in applied machine learning for source code analysis. We review studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, ...
Several libraries in Python, for instance, perform lemmatization, but they have to be tested as their performance significantly differs. Processing text has many applications as now as typing the writer may receive word suggestions. Text classification is one common application to detect email spam, ...
https://machinelearningmastery.com/spot-check-machine-learning-algorithms-in-python/ I have a wonderful codebase I have developed that I might open source one day. CSV in, summary of data prep + model + config that gives good/best results as output. I basically built a ML SaaS for mysel...