Converting words to numbers, Word Embeddings | Deep Learning Tutorial 39 (Tensorflow & Python)
codebasics
11 min, 32 sec
The video explains various methods of converting words into numerical form for Natural Language Processing tasks, focusing on word embeddings.
Summary
- The video begins by reiterating the importance of converting text to numbers for machine learning models as they cannot understand text.
- It presents three methods for word-to-number conversion: assigning unique numbers, one-hot encoding, and using word embeddings.
- Word embeddings are discussed in detail, showcasing how they can effectively capture relationships between words and their features.
- The speaker uses the example of cricket players and terms to illustrate entity recognition and compares features to understand how word embeddings can be derived using machine learning models.
- Word embeddings are preferred for their efficiency and ability to capture semantic relationships, and the video promises future exploration of techniques like TF-IDF and Word2Vec.
Chapter 1
Introduction to the necessity of converting words into numbers for NLP, and setting the stage for the discussion on methods of conversion.
- The video opens with a reference to the previous video on bi-directional RNNs.
- It sets the context for the current topic by stressing the importance of converting text to numbers for NLP tasks.
Chapter 2
Delving into the issue of machine learning models not understanding text and the need for conversion using a cricket game NLP model example.
- The speaker uses the example of recognizing entities like player names, team names, and tournament names in sentences related to cricket.
- The challenge of machine learning models not being able to process text directly is highlighted.
Chapter 3
Explaining the first method of word-to-number conversion by assigning unique numbers to each word based on a vocabulary list.
- A vocabulary is created from scraped internet articles, and each word is assigned a unique number.
- This method is criticized for the randomness of numbers not capturing the relationship between words.
Chapter 4
Discussing one-hot encoding, its process, and highlighting its limitations.
- One-hot encoding is introduced as a method where each word is represented by a vector with one 'hot' entry and the rest as zeros.
- The method's drawbacks include its inability to capture word relationships and its computational inefficiency with large vocabularies.
Chapter 5
Introducing word embeddings as a solution to capture relationships between words through feature vectors.
- Word embeddings are presented as a better alternative that can capture the semantic relationship between words.
- The speaker compares the concept of feature vectors in word embeddings to features used to compare real estate properties.
Chapter 6
Using examples of cricket players and terms, word embeddings are demonstrated to show how words can be compared based on their features.
- The speaker handcrafts features for words like 'Dhoni', 'Cummins', and 'Australia' to illustrate how word embeddings work.
- A feature vector for each word is created, showing how similarities and differences between words can be quantified.
Chapter 7
Summarizing how word embeddings can be applied to solve various NLP tasks and promising future exploration of advanced techniques.
- Word embeddings are linked to their application in NLP tasks, such as sentiment classification and name entity recognition.
- The video concludes by summarizing the three methods of word-to-number conversion and hints at covering TF-IDF and Word2Vec in future videos.