Tech Scouter

Introduction In my previous blog posts, I have written about word vectorization with implementation and use cases.You can read about it  here . But many times we need to mine the relationships between the phrases rather than the sentences. To take an example John has taken many leaves the year Leaves are falling of the tree In these two sentences, a common word "leaves" has a different meaning based on the sentence in which it is used. This meaning can only be captured when we are taking the context of the complete phrase. Or we would like to measure the similarity of the phrases and cluster them under one name. This is going to more of implementation of the doc2vec in python rather than going into the details of the algorithms.  The algorithms use either hierarchical softmax or negative sampling; see  Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean: “Efficient Estimation of Word Representations in Vector Space, in Proceedings of Workshop at ICLR, 20

What is GloVe? GloVe stands for global vectors for word representation. It is an unsupervised learning algorithm developed by Stanford for generating word embeddings by aggregating global word-word co-occurrence matrix from a corpus. The resulting embeddings show interesting linear substructures of the word in vector space. Examples for linear substructures are: These results are pretty impressive. This type of representation can be very useful in many machine learning algorithms. To read more about Word Vectorization you can read my other article . In this blog post, we will be learning about GloVe implementation in python. So, let's get started. Let's create the Embeddings Installing Glove-Python The GloVe is implementation in python is available in library  glove-python. pip install glove_python Text Preprocessing In this step, we will pre-process the text like removing the stop words, lemmatize the words etc. You can perform different steps based on your requirements

Introduction Machine Learning has become the hottest topic in Data Industry with increasing demand for professionals who can work in this domain. There is large amount of textual data present in internet and giant servers around the world. Just for some facts 1,209,600 new data producing social media users each day. 656 million tweets per day! More than 4 million hours of content uploaded to Youtube every day, with users watching 5.97 billion hours of Youtube videos each day. 67,305,600 Instagram posts uploaded each day There are over 2 billion monthly active Facebook users, compared to 1.44 billion at the start of 2015 and 1.65 at the start of 2016. Facebook has 1.32 billion daily active users on average as of June 2017 4.3 BILLION Facebook messages posted daily! 5.75 BILLION Facebook likes every day. 22 billion texts sent every day. 5.2 BILLION daily Google Searches in 2017. Need for Vectorization The amount of textual data is massive, and the problem with textual dat