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首页> 外文会议>Annual meeting of the Association for Computational Linguistics;Conference of the European Chapter of the Association for Computational Linguistics >Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network
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Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network

机译：使用卷积神经网络从注视数据中学习认知特征以进行情感和讽刺分类

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摘要

Cognitive NLP systems- i.e., NLP systems that make use of behavioral data - augment traditional text-based features with cognitive features extracted from eye-movement patterns, EEG signals, brain-imaging etc.. Such extraction of features is typically manual. We contend that manual extraction of features may not be the best way to tackle text subtleties that characteristically prevail in complex classification tasks like sentiment analysis and sarcasm detection, and that even the extraction and choice of features should be delegated to the learning system. We introduce a framework to automatically extract cognitive features from the eye-movement / gaze data of human readers reading the text and use them as features along with textual features for the tasks of sentiment polarity and sarcasm detection. Our proposed framework is based on Convolutional Neural Network (CNN). The CNN learns features from both gaze and text and uses them to classify the input text. We test our technique on published sentiment and sarcasm labeled datasets, enriched with gaze information, to show that using a combination of automatically learned text and gaze features often yields better classification performance over (i) CNN based systems that rely on text input alone and (ii) existing systems that rely on handcrafted gaze and textual features.
机译：认知NLP系统，即利用行为数据的NLP系统，通过从眼动模式，EEG信号，脑成像等中提取的认知特征来增强传统的基于文本的特征。这种特征提取通常是手动的。我们认为，特征的手动提取可能不是解决在诸如情感分析和讽刺检测之类的复杂分类任务中普遍存在的文本细微之处的最佳方法，并且即使特征的提取和选择也应委托给学习系统。我们引入了一个框架，该框架可从阅读文本的人类读者的眼动/凝视数据中自动提取认知特征，并将其与文本特征一起用作情感极性和讽刺检测任务。我们提出的框架基于卷积神经网络（CNN）。 CNN从凝视和文本中学习特征，并使用它们对输入文本进行分类。我们在充斥了注视信息的已发布情绪和讽刺标记数据集上测试了我们的技术，以表明与（i）仅依赖文本输入的基于CNN的系统相比，结合使用自动学习的文字和注视功能通常可以产生更好的分类性能，并且（ ii）依靠手工注视和文字特征的现有系统。

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