Improving the classification of events in tweets using semantic enrichment

Simone Aparecida Pinto Romero, Karin Becker
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引用次数: 10

Abstract

Contextual enrichment using external sources has been proposed as a means to deal with the poor textual contents of tweets for event classification. Related work performs contextual enrichment according to specific assumptions about the events. Furthermore, enrichment adds a significant amount of extra features, most of them with no discriminative contribution to the event classification task. In this paper, we propose an enrichment framework targeted at the classification of events in general, of which the key elements are: a) external enrichment using related web pages for extending the conceptual features contained within the tweets; b) semantic enrichment using the DBpedia to add related semantic features, and c) a pruning technique that selects the semantic features with discriminative potential. We compared the proposed approach against two distinct baselines based on textual features only and word embeddings, using seven different event datasets. Our experiments reveal that the proposed framework supports the classification of distinct event types, outperforming the textual baseline in 63.5% of the cases, and the word embeddings baseline in 96.5% of the cases.
使用语义充实改进tweets中的事件分类
使用外部源的上下文丰富被提出作为一种手段来处理推文的文本内容差的事件分类。相关工作根据对事件的特定假设进行上下文丰富。此外,浓缩增加了大量的额外特征,其中大多数对事件分类任务没有区别性贡献。在本文中,我们提出了一个针对一般事件分类的浓缩框架,其中的关键要素是:a)使用相关网页来扩展tweet中包含的概念特征的外部浓缩;b)使用DBpedia添加相关语义特征的语义丰富,以及c)选择具有判别潜力的语义特征的修剪技术。我们使用七个不同的事件数据集,将所提出的方法与仅基于文本特征和词嵌入的两个不同基线进行了比较。我们的实验表明,该框架支持不同事件类型的分类,在63.5%的情况下优于文本基线,在96.5%的情况下优于词嵌入基线。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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