Natural Language Processing(Part 25)-Vector Space Models
📚Chapter 3: Vector Space Model
Introduction
This Chapter you’re going to learn about vector spaces and specifically you will learn what type of information these vectors could encode. You’ll see different types of applications that you can use with these vector spaces, and you’ll see different types of algorithms you’ll be implementing.
- Vector space models
- Advantages
- Applicon’s
Let’s take a look at an example. In this Tutorial, I’m going to introduce you to the general idea behind vector space models. You’re going to see their advantages along with some of their applications in natural language processing.
In the vast expanse of digital information, finding relevant content is akin to searching for a needle in a haystack. This challenge has given rise to innovative approaches in information retrieval and natural language processing, among which Vector Space Models (VSMs) stand out as powerful tools. In this blog, we’ll delve into the intricacies of Vector Space Models, understanding how they facilitate the representation of textual data in a way that machines can comprehend.
Sections
Understanding Vector Space Models
Why learn vector space models?
Vector space model application
Fundamental Concept
Section 1- Understanding Vector Space Models:
Vector Space Models (VSMs) in Natural Language Processing (NLP) are mathematical frameworks used to represent and analyze textual data in a numerical format. The core idea behind VSMs is to convert words, phrases, or entire documents into vectors within a high-dimensional space, where the geometric relationships between these vectors capture semantic and syntactic similarities. In simpler terms, VSMs provide a means to quantify and compare the meaning of words and documents based on their numerical representations.
Section 2- Why learn vector space models?
So suppose you have two questions, the first one is, where are you heading? And the second one is where are you from? These sentences have identical words except for the last ones. However, they both have a different meaning. On the other hand say you have two more questions whose words are completely different but both sentences mean the same thing.
Vector space models will help you identify whether the first pair of questions or the second pair are similar in meaning even if they do not share the same words. They can be used to identify similarities for a question answering, paraphrasing and summarization.
Section 3- Vector space model application
Vector space models will also allow you to capture dependencies between words. Consider this sentence, you eat cereal from a bowl, here you can see that the words cereal and the word bowl are related. Now let’s look at this other sentence, you buy something and someone else sells it. So what it’s saying is that someone sells something because someone else buys it. The second half of the sentence is dependent on the first half. With vectors based models, you will be able to capture this and many other types of relationships among different sets of words.
Vector space models are used in information extraction to answer questions, in the style of who, what, where, how and etcetera. In machine translation and in chess sports programming. They’re also used in many, many other applications.
Section 4- Fundamental Concept
As a final thought I’d like to share with you this quote from John Firth, a famous English linguists, you shall know a word by the company it keeps. This is one of the most fundamental concepts in NLP. When using vector space models the way that representations are made is by identifying the context around each word in the text and this captures the relative meaning. Eureka, vector space models allow you to represent words and documents as vectors. This captures the relative meaning. You learn about vector space models and you have seen different types of applications where these vector space models are used. In the next video you will build them from scratch and specifically you will see how theyare built using cooccurrence matrices
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Source
1- Natural Language Processing with Classification and Vector Spaces
