论文标题
基于深度学习的端到端隐喻检测希腊语言具有经常性和卷积神经网络
Deep Learning based, end-to-end metaphor detection in Greek language with Recurrent and Convolutional Neural Networks
论文作者
论文摘要
本文介绍并基准测试了许多基于端到端的深度学习模型，用于希腊语中的隐喻检测。我们将卷积神经网络和经常性神经网络与代表性学习相结合，以了解希腊语言的隐喻检测问题。呈现的模型达到了出色的精度得分，从而显着提高了先前的最新结果状态，该结果已经达到了准确性0.82。此外，在这项工作中不使用特殊的预处理，功能工程或语言知识。提供的方法具有卷积神经网络（CNN）和双向长期记忆网络（LSTMS）的0.92和F-评分0.92的精度。通过双向封盖复发单元（GRU）和卷积复发性神经网（CRNN），还可以实现0.91精度和0.91 F评分的可比结果。仅根据培训元素，句子及其标签对模型进行培训和评估。结果是隐喻检测模型的最先进的状态，该模型经过有限的标记资源培训，可以扩展到其他语言和类似的任务。
This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state of the art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of the training tuples, the sentences and their labels. The outcome is a state of the art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.
