论文标题
部分可观测时空混沌系统的无模型预测
Knowledge Graph-Enabled Text-Based Automatic Personality Prediction
论文作者
论文摘要
人们如何思考，感觉和行为，主要是代表他们的性格特征。通过意识到我们正在与之打交道或决定要处理的个人的人格特征，无论其类型如何，人们都可以胜任地改善这种关系。随着基于互联网的通信基础架构（社交网络，论坛等）的兴起，那里发生了相当多的人类通信。这种交流中最突出的工具是以书面和口语形式的语言，可以忠实地编码个人的所有基本人格特征。基于文本的自动人格预测（APP）是基于生成/交换的文本内容对个人个性的自动预测。本文提出了一种基于文本的应用程序的新型知识的方法，该方法依赖于五大人格特征。为此，鉴于文本，知识图是一组相互联系的概念描述，是通过将输入文本的概念与DBPEDIA知识基础条目匹配的。然后，由于实现了更强大的表示，该图被DBPEDIA本体论，NRC情感强度词典和MRC心理语言数据库信息丰富。之后，现在是输入文本的知识渊博的替代方案的知识图被嵌入以产生嵌入矩阵。最后，为了执行人格预测，将所得的嵌入矩阵独立喂入四个建议的深度学习模型，这些模型基于卷积神经网络（CNN），简单的复发神经网络（RNN），长期短期记忆（LSTM）和双向短期长期记忆（Bilstm）。结果表明，所有建议的分类器中的预测准确度有了显着改善。
How people think, feel, and behave, primarily is a representation of their personality characteristics. By being conscious of personality characteristics of individuals whom we are dealing with or decided to deal with, one can competently ameliorate the relationship, regardless of its type. With the rise of Internet-based communication infrastructures (social networks, forums, etc.), a considerable amount of human communications take place there. The most prominent tool in such communications, is the language in written and spoken form that adroitly encodes all those essential personality characteristics of individuals. Text-based Automatic Personality Prediction (APP) is the automated forecasting of the personality of individuals based on the generated/exchanged text contents. This paper presents a novel knowledge graph-enabled approach to text-based APP that relies on the Big Five personality traits. To this end, given a text a knowledge graph which is a set of interlinked descriptions of concepts, was built through matching the input text's concepts with DBpedia knowledge base entries. Then, due to achieving more powerful representation the graph was enriched with the DBpedia ontology, NRC Emotion Intensity Lexicon, and MRC psycholinguistic database information. Afterwards, the knowledge graph which is now a knowledgeable alternative for the input text was embedded to yield an embedding matrix. Finally, to perform personality predictions the resulting embedding matrix was fed to four suggested deep learning models independently, which are based on convolutional neural network (CNN), simple recurrent neural network (RNN), long short term memory (LSTM) and bidirectional long short term memory (BiLSTM). The results indicated a considerable improvements in prediction accuracies in all of the suggested classifiers.
