9 – T-SNE

Word embeddings need to have high dimensionality in order to capture sufficient variations in natural language, which makes them super hard to visualize. T-SNE, which stands for t-Distributed Stochastic Neighbor Embedding, is a dimensionality reduction technique that can map high dimensional vectors to a lower dimensional space. It’s kind of like PCA, Principle Component Analysis, … Read more

8 – Embeddings For Deep Learning

Where the embeddings are fast becoming the de facto choice for representing words, especially for use and deep neural networks. But why do these techniques work so well? Doesn’t it seem almost magical that you can actually do arithmetic with words, like woman minus man plus king equals queen? The answer might lie in the … Read more

7 – GloVe

Word2vec is just one type of forward embedding. Recently, several other related approaches have been proposed that are really promising. GloVe or global vectors for word representation is one such approach that tries to directly optimize the vector representation of each word just using co- occurrence statistics, unlike word2vec which sets up an ancillary prediction … Read more

6 – Word2Vec

Word2Vec is perhaps one of the most popular examples of word embeddings used in practice. As the name Word2Vec indicates, it transforms words to vectors. But what the name doesn’t give away is how that transformation is performed. The core idea behind Word2Vec is this, a model that is able to predict a given word, … Read more

5 – Word Embeddings

One-hot encoding usually works in some situations but breaks down when we have a large vocabulary to deal with, because the size of our ward representation grows with the number of words. What we need as a way to control the size of our word representation by limiting it to a fixed-size vector. In other … Read more

4 – One-Hot Encoding

So far, we’ve looked at representations that tried to characterize an entire document or collection of words as one unit. As a result, the kinds of inferences we can make are also typically at a document level, mixture of topics in the document, documents similarity, documents sentiment, et cetera. For a deeper analysis of text, … Read more

3 – TF-IDF

One limitation of the bag-of-words approach is that it treats every word as being equally important, whereas intuitively, we know that some words occur frequently within a corpus. For example, when looking at financial documents, cost or price may be a pretty common term. We can compensate for this by counting the number of documents … Read more

2 – Bag Of Words

The first feature representation we’ll look at is called Bag of Words. The Bag of Words model treats each document as an un-ordered collection or bag of words. Here, a document is the unit of text that you want to analyze. For instance, if you want to compare essays submitted by students to check for … Read more

10 – NLP Summary

Congratulations on completing the lesson. But remember, this is only the beginning of a long and exciting journey into a world with limitless possibilities. It was a pleasure helping you take your first few steps and I’m looking forward to seeing what systems you will build, what new problems you will solve, and how you’ll … Read more

1 – Feature Extraction

Once we have our text ready in a clean and normalized form, we need to transform it into features that can be used for modeling. For instance, treating each document like a bag of words allows us to compute some simple statistics that characterize it. These statistics can be improved by assigning appropriate weights towards … Read more