A Semi-automatic Data Extraction System for Heterogeneous Data Sources: a Case Study from Cotton Industry

被引：0
|
作者：
Nayak, Richi [1 ,2 ]
Balasubramaniam, Thirunavukarasu [1 ,2 ]
Kutty, Sangeetha [1 ,2 ]
Banduthilaka, Sachindra [3 ]
Peterson, Erin [4 ]
机构：
[1] Queensland Univ Technol, Sch Comp Sci, Brisbane, Qld, Australia
[2] Queensland Univ Technol, Ctr Data Sci, Brisbane, Qld, Australia
[3] Redeye Apps Pvt Ltd, Brisbane, Qld, Australia
[4] Erin Peterson Consulting, Brisbane, Qld, Australia
来源：
DATA MINING, AUSDM 2021 | 2021年 / 1504卷
关键词：
Information extraction; Focused information retrieval; Automated discovery; NER; Chunking; Unstructured data; Web;
D O I：
10.1007/978-981-16-8531-6_15
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
With the recent developments in digitisation, there are increasing number of documents available online. There are several information extraction tools that are available to extract information from digitised documents. However, identifying precise answers to a given query is often a challenging task especially if the data source where the relevant information resides is unknown. This situation becomes more complex when the data source is available in multiple formats such as PDF, table and html. In this paper, we propose a novel data extraction system to discover relevant and focused information from diverse unstructured data sources based on text mining approaches. We perform a qualitative analysis to evaluate the proposed system and its suitability and adaptability using cotton industry.
引用
收藏
页码：209 / 222
页数：14
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