Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships-Naacl 2016-20160422
1、Information
publication:-Naacl 2016
2、What
根据小说中的人物描述,a)在每个时间段给出,人物关系的描述的概率分布,b)从时间轴上看出关系的变化轨迹,提出模型Relationship modeling network.(RMN)
3、Dataset
Project Gutenberg
4、How
input: 时间片段内的小说片段
output: RMN模型中的参数:关系描述的矩阵,关系描述的概率分布
目标函数:小说描述的embeding 尽可能与 关系描述表示的向量接近。
5、Evaluation:baseline:目前没有工作做过,故本论文与topic model 作比较。时间轴上关系变化的评估,与HTMM(hidden topic Markov model )比较
a) 在评价关系描述的学习中,加入干扰项,model peecision.
b)在人物关系轨迹评估中:人工评价 RMN学习出的关系变化 与HTMM学习出的哪个更好
6、Conclusion
近一年文章中Embeding 的方法用的非常多。
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