Natural Language Processing (Part 39)-Searching documents
📚Chapter 4: Machine Translation and Document Search`
Introduction
I will finish this week by showing you how you can use fast k-nearest neighbor to search for pieces of text related to a query in a large collection of documents. You simply create vectors for both and find the nearest neighbors.
Sections
Document representation.
Document Vectors
Section 1. Document representation.
To get ready to perform document search, first, think about how to represent documents as vectors instead of just words as vectors. Let’s say you have these documents composed of three words. I love learning. How can you represent this entire document as a vector?
Well, you can find the word vectors for each individual word. I love learning then just add them together. So the sum of all these word vectors becomes a document vector with the same dimension as the word vectors. In this case, three dimensions. You can then apply document church by using k-nearest neighbors.
Section 2- Document Vectors
Let’s go this up, create a mini dictionary for word embeddings. Here is the list of words contained in the document. You’re going to initialize the document embedding as an area of zeros. Now for each word in a document, you’ll get the word vector if the word exists in the dictionary else zero, you add this all up and return the documents embedding. Please try it out. You learned in this video an example of a very general method that text can be embedded into vector spaces so that nearest neighbors refer to text with similar meaning.
Well, you’ll learn more advanced ways to embed text. This basic structure will reappear again and again and again as it’s used throughout modern NLP.
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Natural Language Processing course
Stay tuned for our upcoming articles where we will explore specific topics related to NLP in more detail!
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To Do List
1- Collects Keys points from the blogs
Source
1- Natural Language Processing with Classification and Vector Spaces
