NLP@UNED在SMM4H 2019:神经网络应用于推文中提到的不利影响的自动分类

Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task Pub Date : 2019-08-01 DOI:10.18653/v1/W19-3213

Javier Cortes-Tejada, Juan Martínez-Romo, Lourdes Araujo

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引用次数: 3

摘要

本文描述了一个自动分类推文中提到的不利影响的系统，该系统是为2019年社交媒体挖掘健康应用(SMM4H)共享任务1开发的。我们开发了一个基于LSTM神经网络的系统，灵感来自于本任务上一版中深度学习分类器获得的出色结果。该网络与Twitter GloVe预训练的词嵌入一起训练。

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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NLP@UNED at SMM4H 2019: Neural Networks Applied to Automatic Classifications of Adverse Effects Mentions in Tweets

This paper describes a system for automatically classifying adverse effects mentions in tweets developed for the task 1 at Social Media Mining for Health Applications (SMM4H) Shared Task 2019. We have developed a system based on LSTM neural networks inspired by the excellent results obtained by deep learning classifiers in the last edition of this task. The network is trained along with Twitter GloVe pre-trained word embeddings.

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Proceedings of the Fourth Social Media Mining for Health Applications (#SMM4H) Workshop & Shared Task

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