Why FastText Embeddings should be used?

FastText offers a significant advantage over traditional word embedding techniques like Word2Vec and GloVe, especially for morphologically rich languages. Here’s a breakdown of how FastText addresses the limitations of traditional word embeddings and its implications:

• Utilization of Character-Level Information: FastText takes advantage of character-level information by representing words as the average of embeddings their character n-grams. This approach allows FastText to capture the internal structure of words, including prefixes, suffixes, and roots, which is particularly beneficial for morphologically rich languages where word formations follow specific rules.
• Extension of Word2Vec Model: FastText is an extension of the Word2Vec model, which means inherits the advantages of Word2Vec, such as capturing semantic relationships between words and producing dense vector representations.
• Handling Out-of-Vocabulary Words: One significant limitation of traditional word embeddings is their inability to handle out-of-vocabulary (OOV) words—words that are not present in the training data or vocabulary. Since Word2Vec and GloVe provide embeddings only for words seen during training, encountering an OOV word during inference can pose a challenge.
• FastText’s Solution for OOV Words: FastText overcomes the limitation of OOV words by providing embeddings for character n-grams. If an OOV word occurs during inference, FastText can still generate an embedding for it based on its constituent character n-grams. This ability makes FastText more robust and suitable for handling scenarios where encountering new or rare words are common, such as social media data or specialized domains.
• Improved Vector Representations for Morphologically Rich Languages: By leveraging character-level information and providing embeddings for OOV words, FastText significantly improves vector representations for morphologically rich languages. It captures only the semantic meaning but also the internal structure and syntactic relations of words, leading to more accurate and contextually rich embeddings.

FastText embeddings are a type of word embedding developed by Facebook’s AI Research (FAIR) lab. They are based on the idea of subword embeddings, which means that instead of representing words as single entities, FastText breaks them down into smaller components called character n-grams. By doing so, FastText can capture the semantic meaning of morphologically related words, even for out-of-vocabulary words or rare words, making it particularly useful for handling languages with rich morphology or for tasks where out-of-vocabulary words are common. In this article, we will discuss about fastText embeddings’ implications in NLP.