Two-Phase Open-Domain Question Answering System

被引：0

作者：

Prasannan, Vysakh ^{[1
]}

Shemshian, Shahin ^{[1
]}

Gurkan, Arinc ^{[1
]}

Saheer, Lakshmi Babu ^{[1
]}

Oghaz, Mahdi Maktabdar ^{[1
]}

机构：

[1] Anglia Ruskin Univ, Cambridge CB1 IPT, England

来源：

ARTIFICIAL INTELLIGENCE XXXIX, AI 2022 | 2022年 / 13652卷

关键词：

Natural language processing; NLP; K-Means; Information retrieval; TF-IDF; Encoder-Decoder; Question answering system;

D O I：

10.1007/978-3-031-21441-7_30

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Text-based Internet content is increasing at a very rapid rate day by day. As a result, even the best search engines are struggling to retrieve the exact expected results of users' queries. On many occasions, the users' expected result is embedded and scattered in a number of different documents and conventional search engines are unable to pinpoint it. To address this shortcoming, this study proposes a two-phased question answering system that utilizes a K-means clustering algorithm alongside the T5 deep encoder-decoder model to formulate a concise short answer to users' queries. The proposed system has been trained using the Kaggle QA and SQuAD datasets and achieved the maximum F1-score of 0.564 and a minimum loss of 8.56.

引用

页码：353 / 358

页数：6

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