Text Mining Meets Neural Nets: Mining the Biomedical Literature by Dan Sullivan.
From the webpage:
Text mining and natural language processing employ a range of techniques from syntactic parsing, statistical analysis, and more recently deep learning. This presentation presents recent advances in dense word representations, also known as word embedding, and their advantages over sparse representations, such as the popular term frequency-inverse document frequency (tf-idf) approach. It also discusses convolutional neural networks, a form of deep learning that is proving surprisingly effective in natural language processing tasks. Reference papers and tools are included for those interested in further details. Examples are drawn from the bio-medical domain.
Basically an abstract for the 58 slides you will find here: http://www.slideshare.net/DanSullivan10/text-mining-meets-neural-nets.
The best thing about these slides is the wealth of additional links to other resources. There is only so much you can say on a slide so links to more details should be a standard practice.
Slide 53: Formalize a Mathematical Model of Semantics, seems a bit ambitious to me. Considering mathematics are a subset of natural languages. Difficult to see how the lesser could model the greater.
You could create a mathematical model of some semantics and say it was all that is necessary, but that’s been done before. Always strive to make new mistakes.