Vector-space word representations have been very successful in recent years at improving performance across a variety of NLP tasks. However, common to most existing work, words are regarded as independent entities without any explicit relationship among morphologically related words being modeled. As a result, rare and complex words are often poorly estimated, and all unknown words are represented in a rather crude way using only one or a few vectors. This paper addresses this shortcoming by proposing a novel model that is capable of building representations for morphologically complex words from their morphemes. We combine recursive neural networks (RNNs), where each morpheme is a basic unit, with neural language models (NLMs) to consider contextual information in learning morphologicallyaware word representations. Our learned models outperform existing word representations by a good margin on word similarity tasks across many datasets, including a new dataset we introduce focused on rare words to complement existing ones in an interesting way.
In the NLP and text mining domain, REF proposed a morphological Recursive Neural Network (morphoRNN) that combines recursive neural networks and neural language models to learn better word representations, in which they regarded each morpheme as a basic unit and leveraged neural language models to consider contextual information in learning morphologically aware word representations.
Better Word Representations with Recursive Neural Networks for Morphology
CoNLL
Computer Science