fabsam @ AMI:卷积神经网络方法(短文)

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020 Pub Date : 1900-01-01 DOI:10.4000/BOOKS.AACCADEMIA.6782

Samuel Fabrizi

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引用次数: 4

摘要

厌恶女性内容的存在是社交网络最关键的问题之一。在本文中，我们介绍了我们在Twitter上识别厌女症的系统。我们的方法是基于卷积神经网络，利用预训练词嵌入。我们还在不同的体系结构之间进行了实验比较，以了解我们的方法的有效性。本文还描述了我们在Evalita 2020自动厌女症识别比赛中提交的子任务A和B。

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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fabsam @ AMI: A Convolutional Neural Network Approach (short paper)

The presence of misogynistic contents is one of the most crucial problems of social networks. In this paper we present our system for misogyny identification on Twitter. Our approach is based on a convolutional neural network that exploits pretrained word embeddings. We also experimented a comparison among different architectures to understand the effectiveness of our method. The paper also described our submissions to both subtasks A and B to Automatic Misogyny Identification competition at Evalita 2020.

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EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

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