Prediction-based Word Embedding Techniques in NLP
Prediction-based embeddings are generated by training models to predict words in a given context. Some popular prediction-based embedding techniques include Word2Vec (Skip-gram and CBOW), FastText, and Global Vectors for Word Representation (GloVe).
- Word2Vec
- Skip-gram
- Predicts surrounding words given a target word.
- Key Features: Learns to represent words that frequently co-occur together, effective for capturing semantic relationships and word analogies.
- CBOW (Continuous Bag of Words)
- Predicts a target word from its context.
- Key Features: Faster to train compared to Skip-gram, useful for generating embeddings for less frequent words.
- Skip-gram
- FastText
- Enhances Word2Vec by incorporating sub-word information (character n-grams) into word embeddings.
- Key Features: Captures word morphological similarity, handles misspellings and unseen words effectively.
- GloVe (Global Vectors for Word Representation)
- Utilizes global word co-occurrence data from the entire corpus to identify word vectors.
- Combines local context windows and applies matrix factorization algorithms to create high-quality embeddings.
- Key Features: Uses statistics on worldwide word co-occurrence, works well for encoding word analogies and semantic links.
Prediction-based embeddings are valuable for capturing semantic relationships and contextual information in text, making them useful for a variety of NLP tasks such as machine translation, sentiment analysis, and document clustering.
Word Embedding Techniques in NLP
Word embedding techniques are a fundamental part of natural language processing (NLP) and machine learning, providing a way to represent words as vectors in a continuous vector space. In this article, we will learn about various word embedding techniques.
Table of Content
- Importance of Word Embedding Techniques in NLP
- Word Embedding Techniques in NLP
- 1. Frequency-based Embedding Technique
- 2. Prediction-based Embedding Techniques
- Other Word Embedding Techniques
- FAQs on Word Embedding Techniques
Word embeddings enhance several natural language processing (NLP) steps, such as sentiment analysis, named entity recognition, machine translation, and document categorization.