计算机科学 ›› 2018, Vol. 45 ›› Issue (9): 243-247.doi: 10.11896／j.issn.1002-137X.2018.09.040
周艳芳, 周刚, 鹿忠磊
ZHOU Yan-fang, ZHOU Gang, LU Zhong-lei
摘要： 立场分析旨在发现用户对特定目标对象所持的观点态度。针对现有方法往往难以克服标注数据匮乏及微博文本中大量未登录词等导致的分词误差的问题,提出了基于迁移学习及字、词特征混合的立场分析方法。首先,将字、词特征输入深度神经网络,级联两者隐藏层输出,复现由分词错误引起的缺失语义信息;然后,利用与立场相关话题的辅助数据训练话题分类模型(父模型),得到更为有效的句子特征表示;接着,以父模型参数初始化立场分析模型(子模型),从辅助数据(话题分类数据)迁移知识能加强句子的语义表示能力;最后,使用有标注数据微调子模型参数并训练分类器。在NLPCC-2016任务4的语料上进行实验,F1值达72.2%,优于参赛团队的最佳成绩。实验结果表明,该方法可提高立场分类性能,同时缓解分词误差带来的影响。
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