ConvNet Seq2seq for Machine Translation

In these few days, Facebook published a new research paper, regarding the use of sequence to sequence (seq2seq) model for machine translation. What is special about this seq2seq model is that it uses convolutional neural networks (ConvNet, or CNN), instead of recurrent neural networks (RNN). The original seq2seq model is implemented with Long Short-Term Memory … More ConvNet Seq2seq for Machine Translation

Computational Journalism

We “sensed” what has been the current hot issues in the past (and we still often do today.) Methods of “sensing,” or “detecting”, is now more sophisticated however as the computational technologies are now more advanced. The methods involved can be collected to a field called “computational journalism.” Recently, there is a blog post by … More Computational Journalism

Rapalytics

Text mining can be applied on rap lyrics. Today I attended an event organized by Data Science MD Meetup Group, a talk titled “Lose Yourself in Rapalytics,” by Abhay, a PhD student in University of Maryland, Baltimore County (UMBC). Rapalytics is an online tool analyzing raps. It is another task of text mining and natural … More Rapalytics

Toying with Word2Vec

One fascinating application of deep learning is the training of a model that outputs vectors representing words. A project written in Google, named Word2Vec, is one of the best tools regarding this. The vector representation captures the word contexts and relationships among words. This tool has been changing the landscape of natural language processing (NLP). Let’s … More Toying with Word2Vec

Talking Not So Deep About Deep Learning

On October 14, 2015, I attended the regular meeting of the DCNLP meetup group, a group on natural language processing (NLP) in Washington, DC area. The talk was titled “Deep Learning for Question Answering“, spoken by Mr. Mohit Iyyer, a Ph.D. student in Department of Computer Science, University of Maryland (my alma mater!). He is a … More Talking Not So Deep About Deep Learning

EMBERS: predicting civil unrest real-time

I heard about this project, EMBERS (acronym to Early Model Based Event Recognition using Surrogates), in a DC Data Science meetup. The speaker was Naren Ramakrishnan from VirginiaTech. To me, it is a real big data project. It is a software that forecasts massive atrocities, particularly on civil unrest (mainly in Latin America and Middle East). They … More EMBERS: predicting civil unrest real-time