AI-Accelerated Product Development
Vector Space Model of Word Representation in NLP
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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 ?
Data Scientist new to NLP
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to see details.
1. Vector space model
Quick introduction to vector space model and basic technical concepts
Technical concepts explained with examples such as term frequency, cosine similarity, inverse document frequency
3. IF-IDF implementation in python
How to implement TF-IDF embedding with NLTK in python?
This section from chapter clearly explains dense semantic model and how Word2Vec model works?
5. Word2Vec Implementation
Word2Vec model is implemented from scratch which can help to learn and understand concept clearly.
6. Vector Semanics
Clear explanation of basic concepts such as word meaning, vectors and embedding with simple examples
7. Lexical Semantics
Covers basic principles of word meaning required to understand vector semantics, embedding and vector space model