Word Mover’s Distance as a Linear Programming Problem

Much about the use of word-embedding models such as Word2Vec and GloVe have been covered. However, how to measure the similarity between phrases or documents? One natural choice is the cosine similarity, as I have toyed with in a previous post. However, it smoothed out the influence of each word. Two years ago, a group … More Word Mover’s Distance as a Linear Programming Problem

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