This shortlist will help user the basic concepts of vector semantics, word and vectors, embedding. In addition learn types vector space models namely the tf-idf and Word2vec.

Potential Use Cases

Vector space models are used in information filtering, information retrieval, plagiarism detection, news recommender system, comparing, indexing and relevancy rankings of text documents.

Who is This For ?

BEGINNERData Scientist new to NLP

Click on each of the following annotated items to see details.

Resource Asset5/7

VIDEO 1. Vector space model

Quick introduction to vector space model and basic technical concepts

10 minutes

BOOK_CHAPTER 2. TF-IDF

Technical concepts explained with examples such as term frequency, cosine similarity, inverse document frequency

30 minutes

ARTICLE 3. IF-IDF implementation in python

How to implement TF-IDF embedding with NLTK in python?

20 minutes

BOOK_CHAPTER 4. Word2Vec

This section from chapter clearly explains dense semantic model and how Word2Vec model works?

20 minutes

ARTICLE 5. Word2Vec Implementation

Word2Vec model is implemented from scratch which can help to learn and understand concept clearly.

9 minutes

VIDEO 6. Vector Semanics

Clear explanation of basic concepts such as word meaning, vectors and embedding with simple examples

15 minutes

BOOK_CHAPTER 7. Lexical Semantics

Covers basic principles of word meaning required to understand vector semantics, embedding and vector space model