Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification
Senti17 at SemEval-2017 Task 4: Ten Convolutional Neural Network Voters for Tweet Polarity Classification
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Senti17 参加 SemEval-2017 任务 4：用于推文极性分类的十个卷积神经网络投票者
DOI：
10.18653/v1/s17-2116
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
H. Hamdan
中科院分区：
文献类型：
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作者：
H. Hamdan
本文提出了Senti17系统，该系统使用十个卷积神经网络（ConvNet）为tweet分配情感标签。该网络由一个卷积层和一个全连接层组成，最上面是一个Softmax。该网络的十个实例使用相同的词嵌入作为输入进行初始化，但对网络权重进行了不同的初始化。我们通过选择10个投票人中的大多数给出的情绪标签来组合所有实例的结果。该系统在SemEval-2017 Task4中排名第四，超过38个系统，平均召回率为67.4%。
This paper presents Senti17 system which uses ten convolutional neural networks (ConvNet) to assign a sentiment label to a tweet. The network consists of a convolutional layer followed by a fully-connected layer and a Softmax on top. Ten instances of this network are initialized with the same word embeddings as inputs but with different initializations for the network weights. We combine the results of all instances by selecting the sentiment label given by the majority of the ten voters. This system is ranked fourth in SemEval-2017 Task4 over 38 systems with 67.4% average recall.