Keyphrase extraction as a task to identify important words or phrases from a
text, is a crucial process to identify main topics when analyzing texts from a
social media platform. In our study, we focus on text written in Indonesia
language taken from Twitter. Different from the original joint layer recurrent
neural network (JRNN) with output of one sequence of keywords and using only
word embedding, here we propose to modify the input layer of JRNN to extract
more than one sequence of keywords by additional information of syntactical
features, namely part of speech, named entity types, and dependency structures.
Since JRNN in general requires a large amount of data as the training examples
and creating those examples is expensive, we used a data augmentation method to
increase the number of training examples. Our experiment had shown that our
method outperformed the baseline methods. Our method achieved .9597 in accuracy
and .7691 in F1.

本研究旨在提高从印尼 Twitter 数据中提取关键词的精确度，使用句法特征等额外信息修改 JRNN 模型的输入层提取多个关键词序列，并使用数据增强方法增加训练示例。在实验证明该方法优于基线方法，精度和 F1 分别达到 0.9597 和 0.7691。