FastText: An Overview of Subword-based Word Embeddings
FastText is a type of word embedding that utilizes subword information. Word embeddings are numerical representations of words that allow machines to understand natural language. They help improve the performance of various natural language processing (NLP) tasks, such as sentiment analysis, text classification, and machine translation.
What are Word Embeddings?
Word embeddings are numerical representations of words that capture their meanings and relationships with other words. These embeddings are trained using unsupervised machine learning algorithms that learn to predict the context in which a word appears in a text corpus.
For example, a simple word embedding model like Word2Vec would represent the word "king" as a vector of numbers. This vector would be calculated by looking at the words that are likely to appear in the same context as "king," such as "queen," "throne," and "royal." Therefore, the vector for "king" would have high values for dimensions that represent these words.
Word embeddings have been proved to be very useful in various natural language processing tasks. However, these embeddings do not consider the subword information in a word, such as prefixes and suffixes. This can be a problem for languages with complex morphology, where words can have different meanings depending on their prefixes and suffixes.
What are Subword-based Word Embeddings?
Subword-based word embeddings, such as FastText, use subword information to construct word embeddings. These embeddings are learned by considering the character $n$-grams of the word, where an $n$-gram is a contiguous sequence of $n$ characters.
For example, the word "nature" would be represented as a summation of its character $n$-grams, such as "nat," "atu," "tur," and "ure." This allows the embeddings to capture the meaning of the prefix "nat-" and the suffix "-ure."
Once a word has been represented as a summation of its character $n$-grams, a skipgram model is trained to learn the embeddings. This model learns to predict the context in which the word appears, given the embeddings of its character $n$-grams.
What are the Benefits of Using FastText?
FastText has several benefits over traditional word embeddings:
- Improved performance for languages with complex morphology: Languages like Finnish, Turkish, and Russian have complex morphology, where words can have many different forms depending on their prefixes and suffixes. FastText can capture these variations by considering subword information.
- Efficient for rare and out-of-vocabulary words: Traditional word embeddings cannot represent words that are not present in the training data. FastText can still represent these words by considering their subword information. This is especially useful for languages with many rare and complex words.
- Improved performance for small datasets: Traditional word embeddings require a large amount of data to be trained effectively. FastText can still generate high-quality embeddings with smaller datasets, which is useful for applications with limited data.
In summary, FastText is a type of subword-based word embedding that allows machines to understand natural language more effectively. By considering subword information, FastText can capture the meanings of prefixes and suffixes, which is useful for languages with complex morphology. FastText has several benefits over traditional word embeddings, including improved performance for rare and out-of-vocabulary words and small datasets.
