Techniques To Create Word Embedding : Slides and Guide

Here is a comprehensive comparison of four Natural Language Processing (NLP) techniques: Word2Vec, GloVe, BERT, and RoBERTa. See detail and browse slides on Embeddings. It discusses their functionalities, latest advancements, and selection criteria, highlighting that Word2Vec is suitable for semantic understanding in smaller datasets, GloVe for capturing global corpus statistics, BERT for deep context understanding, and RoBERTa for large datasets requiring deep context comprehension.
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Technique Description Latest Methods Selection Criteria
Word2Vec Word2Vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. FastText is an extension to Word2Vec proposed by Facebook in 2016. Instead of feeding individual words into the Neural Network, FastText breaks words into several n-grams. Word2Vec is a good choice when the text data is not extremely large and when semantic meaning of words is of high importance.
GloVe GloVe, short for Global Vectors, is a model for distributed word representation. The model is an unsupervised learning algorithm for obtaining vector representations for words. GloVe has not seen much advancement since its inception. However, it is often used in combination with other methods like Neural Networks for better performance. GloVe is a good choice when there is a need to capture global statistics of the corpus.
BERT BERT, or Bidirectional Encoder Representations from Transformers, is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. RoBERTa, a robustly optimized BERT approach by Facebook, modifies key hyperparameters in BERT to improve its performance. BERT is a good choice when there is a need for a deep understanding of context and the order of words in text data.
RoBERTa RoBERTa builds on BERT by adjusting the training methodology and removing the next sentence prediction mechanism from BERT. It uses a larger byte-level BPE vocabulary and a larger batch size for training. There are no significant advancements in RoBERTa as of now. However, it is often used in combination with other methods for better performance. RoBERTa is a good choice when there is a need for a deep understanding of context and the order of words in text data, and when the training data is large.



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