Principle-based Approach for Semi-automatic Construction of a Restaurant Question Answering System from Limited Datasets

被引：1

作者：

Yang, Ting-Hao ^{[1
,2
]}

Hsieh, Yu-Lun ^{[1
,2
]}

Chung, You-Shan ^{[2
]}

Shih, Cheng-Wei ^{[2
]}

Liu, Shih-Hung ^{[2
]}

Chang, Yung-Chun ^{[2
]}

Hsu, Wen-Lian ^{[2
]}

机构：

[1] Natl Tsing Hua Univ, Inst Informat Syst & Applicat, Hsinchu, Taiwan

[2] Acad Sinica, Inst Informat Sci, Taipei, Taiwan

来源：

PROCEEDINGS OF 2016 IEEE 17TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IEEE IRI) | 2016年

关键词：

Ontology; Alignment; Dominating Set;

D O I：

10.1109/IRI.2016.77

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Question answering (QA) is an important research issue in natural language processing, and most state-of-the-art question answering systems are based on statistical models. After wit nessing recent achievements in Artificial Intelligent (AI), many businesses wish to apply those techniques to an automatic QA system that is capable of providing 24-hour customer services for their clients. However, o ne imminent problem is the lack of labeled training data for the specific domain. To address this issue, we propose to combine a knowledge-based approach and an automatic principle generation process to build a QA system from limited resources. Experiments conducted on a Mandarin Restaurant dataset show that our system achieves an average accuracy of 44% for 10 question types. It demonstrates that our approach can provide an effective tool when creating a QA system.

引用

页码：520 / 524

页数：5