Machine Learning, AI and Programming

Tag: Word Vectors

Using Word Vectors in Multi-Class Text Classification

Earlier we have seen how instead of representing words in a text document as isolated features (or as N-grams), we can encode them into multidimensional vectors where each dimension of the vector represents some kind semantic or relational similarity with other words in the corpus. Machine Learning problems such as classification or clustering, requires documents to be represented as a document-feature matrix (with TF or TF-IDF weighting), thus we need some […]

Continue Reading →

Designing a Contextual Graphical Model for Words

I have been reading about Word Embedding methods that encode words found in text documents into multi-dimensional vectors. The purpose of encoding into vectors is to give "meaning" to words or phrases in a context. Traditional methods of document classification treat each word in isolation or at-most use a N-gram approach i.e. in vector space, the words are represented as one-hot vectors which are sparse and do not convey any meaning whereas […]

Continue Reading →

Understanding Word Vectors and Word2Vec

Quite recently I have been exploring the Word2Vec tool, for representing words in text documents as vectors. I got the initial ideas about word2vec utility from Google's code archive webpage. The idea behind coming up with this kind of utility caught my interest and later I went on to read the following papers by Mikolov et. al. to better understand the algorithm and its implementation. Efficient Estimation of Word Representations […]

Continue Reading →