Other Word Embedding Techniques
Other Word Embedding Techniques include the following:
- ELMO (Embeddings from Language Models): Contextual word embeddings based on character-based word representations and bidirectional LSTMs.
- ULMFiT (Universal Language Model Fine-tuning): Pretrained language model followed by fine-tuning on specific tasks.
- GPT (Generative Pre-trained Transformer): Transformer-based language model that can be used for word embeddings.
- Transformer-XL: Extension of the transformer model with recurrence to handle longer context.
- Swivel: An unsupervised model that creates embeddings based on co-occurrence statistics similar to Word2Vec but operates on a different principle.
- Para2Vec: Embedding technique that learns embeddings for sentences and paragraphs, not just words.
- Skip-Thought Vectors: Unsupervised learning to generate sentence embeddings by predicting surrounding sentences.
- Sentence-BERT: Modification of BERT for sentence embeddings.
- USE (Universal Sentence Encoder): Encoder that creates embeddings for sentences and phrases using transformer architectures.
- Doc2Vec: Extends Word2Vec to learn embeddings for entire documents or sentences.
- LDA (Latent Dirichlet Allocation): A generative probabilistic model used for topic modeling that can be used to create embeddings based on topic distributions.
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.