Distributional Semantic Representation in Health Care Text Classification
Distributional Semantic Representation in Health Care Text Classification
复制标题
医疗保健文本分类中的分布语义表示
作者：
B. Ganesh;Hb;Anand Kumar;Soman Kp
本文介绍了我们在消费者健康信息搜索(CHIS)任务中提出的系统。任务1的目标是将文档中的句子分类为与查询相关或不相关，而任务2正在分析文档中的句子相对于给定查询的情感。在该方法中，文本的分布表示及其统计和距离度量作为一个文本分类问题来执行给定的任务。在我们的实验中，利用非负矩阵分解得到文档的分布式表示，并以查询、距离和相关性度量作为特征，利用随机森林树进行分类。该方法在任务1和任务2中的平均准确率分别为70.19%和34.64%。
This paper describes about the our proposed system in the Consumer Health Information Search (CHIS) task. The objective of the task 1 is to classify the sentences in the document into relevant or irrelevant with respect to the query and task 2 is analysing the sentiment of the sentences in the documents with respect to the given query. In this proposed approach distributional representation of text along with its statistical and distance measures are carried over to perform the given tasks as a text classification problem. In our experiment, Non Negative Matrix Factorization utilized to get the distributed representation of the document as well as queries, distance and correlation measures taken as the features and Random Forest Tree utilized to perform the classification. The proposed approach yields 70.19% in task 1 and 34.64% in task 2 as an average accuracy.