Authors: Brad Jascob
In the field of Language Modeling, neural-network models have become popular due to their ability to reach low Perplexity scores. A common approach to training these models is to use a large corpus, such as the Billion Word Corpus, and restrict the vocabulary to the top-N most common words (aka tokens). The less common words are then replaced with an “unknown” token. These unknown tokens then become a single representation for all low occurrence words which may not be closely related semantically. In addition, some closely related tokens, such as numbers, may be common enough to be given a unique integer ID when we might prefer that they be combined under a single ID. In the following article, we’ll explore using part-of-speech (POS) tagging to identify word types and then use this information to create a “smarter” vocabulary. Using this smarter vocabulary, we’ll show that it achieves a lower perplexity score, for a given epoch, than a similar model using a top-N type vocabulary.
Comments: 5 Pages.
[v1] 2019-01-22 08:30:24
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