Global Vectors for Word representation (GloVe) is a model for word representation. It falls under the category of unsupervised learning. It learns from developing a count matrix for word occurrence. Initially, it starts with the large matrix to store almost all the words and their co-occurrence information, which stores the count of how frequently some words appear in the sequence in given text. Support for GloVe is available in Stanford NLP, but is not implemented in Java. To read more about GloVe, visit https://nlp.stanford.edu/pubs/glove.pdf. A brief introduction and some resources for the Stanford GloVe can be found at https://nlp.stanford.edu/projects/glove/. To get an idea of what GloVe does, we will be using a Java implementation of GloVe found at https://github.com/erwtokritos/JGloVe .
The code also includes the test file and a text file. The text file's contents are as follows:
human interface computer survey user computer system response time eps user interface system system...