Methods for Unsupervised Semantic Modelling (by Professor Wray Buntine)

(11/1/2016)

The seminar given by Prof Wray shared some non-parametric methods for unsupervised modelling based on an example in NLP (natural language processing). With current growth of information systems, we need some better tools to help us to deal with the information overload. Such tools should be capable of organizing, searching, summarizng and even understanding the information. The 'understanding of the meaning of natural language' or in other word 'semantic' is the main focus of Prof Wray's methods.

The unsupervised learning in this model is based on Dirichlet distribution from probability and statistic. Throughout the presentation, Prof Wray tried to avoid the complex maths functions he has used. Basically, the concept involves probability vectors for the following elements quoted from Prof Wray's note:

• the next word given (n − 1) previous, 
• an author/conference/corporation to be linked to/from a webpage/patent/citation, 
• part-of-speech of a word in context, 
• hashtag in a tweet given the author.

A Dirirhlet distribution is then used to develop Dirichlet processes for the semantic model. It enable the approximation of vocabularies or documents hierarchically. The benefit of this model as per said by Prof Wray is that it reduces parameters optimisation problem faced by most distribution functions. Also, the nested (or hierarchical) Dirichlet processes have fast samplers as compared to others.

This is indeed a very high level of learning for me as the concepts and functions involved are really something new to me. Anyway, thanks to the seminar, I am more open to some new and advanced algorithms for my research project.

Communicating Research (Workshop 4)

This workshop is the last of the series of Communicating Research in 2015, and it focused on profiling our research. Research profile, when constructed with the right amount of details, would become a basic tool to promote our research across different digital media.

In this workshop, we were told about some common social media that we may consider for promoting our profile. Examples of academic/research social media are Research Gate, Acedmia.edu and Google Scholar. The non-academic profiling media could be LinkedIn or even Facebook. I have personally joined Research Gate a few months ago, in which I find that it is quite a good tool in the way that it tries to connect you with other researchers when common research areas are found. But I don't really like to use Facebook or or other non-academic social media for the similar purpose as I feel that people within my Facebook contact are generally not researchers. However, after some thoughts shared by Julie Holden, I do agree that other social media like Facebook, if organized and managed properly, can be a good place to promote our research as well.

Knowing the benefit of profiling our research, especially with the help of social media, I begin to fill-in more details in Research Gate and start organizing my Facebook contents. Hopefully, as suggested by this workshop, our profile will benefit us not only in our career, but also gets our findings and works reaching out to more people, inclusive of even friends and family members who aren't experts in our field. One day, they are going to understand and appreciate our works.

PS: At the end of the workshop, we were asked to complete our profile based on a template provided to us, and with delight, the profile is going to be published at Monash website! That's great.