Kavita’s Articles

Word2Vec is a widely used word representation technique that uses neural networks under the hood. The resulting word representation or embeddings can be used to infer semantic similarity between words and phrases, expand queries, surface related concepts…

Previously, we learned how to use CountVectorizer for text processing. In place of CountVectorizer, you also have the option of using HashingVectorizer. In this tutorial, we will learn how HashingVectorizer differs from CountVectorizer and when to use…

Most text classification examples that you see on the Web or in books focus on demonstrating techniques. This will help you build a pseudo usable prototype. If you want to take your classifier to the next level…

Companies receive support enquiries from various channels. This may include emails, support tickets, tweets, chat conversations with customer support representatives (CSRs), chatbot conversations and more. Figure 1: Sources of customer support enquiries This is a lot of…

Scikit-learn’s CountVectorizer is used to transform a corpora of text to a vector of term / token counts. It also provides the capability to preprocess your text data prior to generating the vector representation making it a…

Text classification is the automatic process of predicting one or more categories given a piece of text. For example, predicting if an email is legit or spammy. Thanks to Gmail’s spam classifier, I don’t see or hear…

What are pre-trained embeddings and why? Pre-trained word embeddings are vector representation of words trained on a large dataset. With pre-trained embeddings, you will essentially be using the weights and vocabulary from the end result of the…

Scikit-learn’s Tfidftransformer and Tfidfvectorizer aim to do the same thing, which is to convert a collection of raw documents to a matrix of TF-IDF features. The differences between the two modules can be quite confusing and it’s…

Learn what text preprocessing is, the different techniques for text preprocessing and a way to estimate how much preprocessing you may need. For those interested, I’ve also made some text preprocessing code snippets in python for you…

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Inverse Document Frequency (IDF) is a weight indicating how commonly a word is used. The more frequent its usage across documents, the lower its score. The lower the score, the less important the word becomes. For example,…

Term Frequency (TF) Term frequency (TF) often used in Text Mining, NLP and Information Retrieval tells you how frequently a term occurs in a document. In the context natural language, terms correspond to words or phrases. Since…

N-grams of texts are extensively used in text mining and natural language processing tasks. They are basically a set of co-occurring words within a given window and when computing the n-grams you typically move one word forward…

Having spent a big part of my career as a graduate student researcher and now a Data Scientist in the industry, I have come to realize that a vast majority of solutions proposed both in academic research…

About a year ago, I looked high and low for a python word cloud library that I could use from within my Jupyter notebook that was flexible enough to use counts or tfidf when needed or just…

Document frequency is the number of documents containing a particular term. Based on Figure 1, the word cent has a document frequency of 1. Even though it appeared 3 times, it appeared 3 times in only one…

In this era of use Deep Learning for everything, one may be wondering why you would even use TF-IDF for any task at all ?!! The truth is TF-IDF is easy to understand, easy to compute and…

Stop words are a set of commonly used words in any language. For example, in English, “the”, “is” and “and”, would easily qualify as stop words. In NLP and text mining applications, stop words are used to…

Training a Word2Vec model with phrases is very similar to training a Word2Vec model with single words. The difference: you would need to add a layer of intelligence in processing your text data to pre-discover phrases. In…

ROUGE 2.0 is an easy to use evaluation toolkit for Automatic Summarization tasks. It uses the ROUGE system of metrics which works by comparing an automatically produced summary or translation against a set of reference summaries (typically human-produced). ROUGE…