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

Short Text Categorization using Deep Neural Networks and Word-Embedding Models

There are situations that we deal with short text, probably messy, without a lot of training data. In that case, we need external semantic information. Instead of using the conventional bag-of-words (BOW) model, we should employ word-embedding models, such as Word2Vec, GloVe etc. Suppose we want to perform supervised learning, with three subjects, described by … More Short Text Categorization using Deep Neural Networks and Word-Embedding Models

Simple Literary Analytics on Presidential Candidates in the First 2016 Presidential Debate

The first presidential debate 2016 was held on September 26, 2016 in Hofstra University in New York. An interesting analysis will be the literacy level demonstrated by the two candidates using Flesch readability ease and Flesch-Kincaid grade level, demonstrated in my previous blog entry and my Github: stephenhky/PyReadability. First, we need to get the transcript of the … More Simple Literary Analytics on Presidential Candidates in the First 2016 Presidential Debate

SOCcer: Computerized Coding In Epidemiology

There are many tasks that involve coding, for example, putting kids into groups according to their age, labeling the webpages about their kinds, or putting students in Hogwarts into four colleges… And researchers or lawyers need to code people, according to their filled-in information, into occupations. Melissa Friesen, an investigator in Division of Cancer Epidemiology … More SOCcer: Computerized Coding In Epidemiology