论文标题
文章引文研究：上下文增强了引用情感检测
Article citation study: Context enhanced citation sentiment detection
论文作者
论文摘要
引用列赛分析是研究的小型研究任务之一。为了引用分析，我们开发了八个包含引文句子的数据集，这些数据集由我们手动注释为三个情感极性。积极，负和中立。在八个数据集中，三个是通过考虑引用的整个背景来开发的。此外，我们提出了一种连接的功能工程方法，其中包含用于文本，词性exhich标签和依赖关系的单词嵌入。结合功能被认为是基于深度学习的引用情感分类方法的输入，这又与单词袋方法相比。实验结果表明，深度学习对于更高数量的样本很有用，而支持向量机是少量样本的赢家。此外，证明基于上下文的样本比无上下文的样本更有效，用于引用情感分析。
Citation sentimet analysis is one of the little studied tasks for scientometric analysis. For citation analysis, we developed eight datasets comprising citation sentences, which are manually annotated by us into three sentiment polarities viz. positive, negative, and neutral. Among eight datasets, three were developed by considering the whole context of citations. Furthermore, we proposed an ensembled feature engineering method comprising word embeddings obtained for texts, parts-of-speech tags, and dependency relationships together. Ensembled features were considered as input to deep learning based approaches for citation sentiment classification, which is in turn compared with Bag-of-Words approach. Experimental results demonstrate that deep learning is useful for higher number of samples, whereas support vector machine is the winner for smaller number of samples. Moreover, context-based samples are proved to be more effective than context-less samples for citation sentiment analysis.
