A simple method is to split into characters and then learn to transform this character sequence into a vector representation by feeding it to a convolutional neural network (CNN) or a recurrent neural network (RNN), usually a long-short term memory (LSTM). There are many ways of splitting a word into subwords. For example, the suffix -shire lets you guess that Melfordshire is probably a location, or the suffix -osis that Myxomatosis might be a sickness. Subword approaches try to solve the unknown word problem differently, by assuming that you can reconstruct a word's meaning from its parts. A makeshift solution is to replace such words with an token and train a generic embedding representing such unknown words. If you are using word embeddings like word2vec or GloVe, you have probably encountered out-of-vocabulary words, i.e., words for which no embedding exists. What are subword embeddings and why should I use them? The embed method combines encoding and embedding lookup:

> emb_layer(tensor(ids)).shape # in PyTorch To perform an embedding lookup, encode the text into byte-pair IDs, which correspond to row indices in the embedding matrix: > emb_layer = nn.om_pretrained(tensor(bpemb_zh.vectors))

Use these vectors as a pretrained embedding layer in your neural model. The actual embedding vectors are stored as a numpy array: # īPEmb objects wrap a gensim KeyedVectors instance: > type(bpemb_zh.emb) > bpemb_zh.encode('这是一个中文句子') # 'This is a Chinese sentence.' Load a Chinese model with vocabulary size 100,000: Pre-trained byte-pair embeddings work surprisingly well, while requiring no tokenization and being much smaller than alternatives: an 11 MB BPEmb English model matches the results of the 6 GB FastText model in our evaluation.In this case the BPE segmentation might be something like melf ord shire. Byte-Pair Encoding gives a subword segmentation that is often good enough, without requiring tokenization or morphological analysis.E.g., the suffix -shire in Melfordshire indicates a location.

Subwords allow guessing the meaning of unknown / out-of-vocabulary words.

Its intended use is as input for neural models in natural language processing. BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia.

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