Abstract
Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are unpredictable with respect to their component words. In this paper we propose the first deep learning models for token-level identification of MWEs. Specifically, we consider a layered feedforward network, a recurrent neural network, and convolutional neural networks. In experimental results we show that convolutional neural networks are able to outperform the previous state-of-the-art for MWE identification, with a convolutional neural network with three hidden layers giving the best performance.
Highlights
Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are idiomatic, i.e., marked or unpredictable, with respect to properties of their component words (Baldwin and Kim, 2010)
The challenges posed by MWEs have led to them to be referred to as a “pain in the neck” for natural language processing (NLP) (Sag et al, 2002); incorporating knowledge of MWEs into NLP applications can lead to improvements in tasks including machine translation (Carpuat and Diab, 2010), information retrieval (Newman et al, 2012), and opinion mining (Berend, 2011)
Recent work on token-level MWE identification has focused on methods that are applicable to the full spectrum of kinds of MWEs (Schneider et al, 2014a), in contrast to earlier work that tended to focus on specific kinds of MWEs (Uchiyama et al, 2005; Fazly et al, 2009; Fothergill and Baldwin, 2012)
Summary
Multiword expressions (MWEs) are lexical items that can be decomposed into multiple component words, but have properties that are idiomatic, i.e., marked or unpredictable, with respect to properties of their component words (Baldwin and Kim, 2010). Deep learning is an emerging class of machine learning models that have recently achieved promising results on a range of NLP tasks such as machine translation (Bahdanau et al, 2015; Sutskever et al, 2014), named entity recognition (Lample et al, 2016), natural language generation (Li et al, 2015), and sentence classification (Kim, 2014). Such models have, not yet been applied to broad-coverage MWE identification
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