Verb-noun idiomatic combinations (VNICs) are idioms consisting of a verb with a noun in its direct object position. Usages of these expressions can be ambiguous between an idiomatic usage and a literal combination. In this paper we propose supervised and unsupervised approaches, based on word embeddings, to identifying token instances of VNICs. Our proposed supervised and unsupervised approaches perform better than the supervised and unsupervised approaches of Fazly et al. (2009), respectively.

This paper proposes a neural network architecture for tackling the query-by-example user-defined keyword spotting task. A multi-head attention module is added on top of a multi-layered GRU for effective feature extraction, and a normalized multi-head attention module is proposed for feature aggregation. We also adopt the softtriple loss -a combination of triplet loss and softmax loss -and showcase its effectiveness. We demonstrate the performance of our model on internal datasets with different languages and the public Hey-Snips dataset. We compare the performance of our model to a baseline system [1] and conduct an ablation study to show the benefit of each component in our architecture. The proposed work shows solid performance while preserving simplicity.

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.

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In this paper we consider several approaches to predicting semantic textual similarity using word embeddings, as well as methods for forming embeddings for larger units of text. We compare these methods to several baselines, and find that none of them outperform the baselines. We then consider both a supervised and unsupervised approach to combining these methods which achieve modest improvements over the baselines.