Extracting Tabular data for Question-Answering from Documents

被引：0

作者：

Jain, Palak ^{[1
]}

Goel, Tushar ^{[1
]}

Verma, Ishan ^{[1
]}

Shakir, Mohammad ^{[1
]}

Dey, Lipika ^{[1
]}

Sharma, Geetika ^{[1
]}

机构：

[1] TCS Res & Innovat, Pune, Maharashtra, India

来源：

CODS-COMAD 2021: PROCEEDINGS OF THE 3RD ACM INDIA JOINT INTERNATIONAL CONFERENCE ON DATA SCIENCE & MANAGEMENT OF DATA (8TH ACM IKDD CODS & 26TH COMAD) | 2021年

关键词：

Table Detection; Tabular data Extraction; Query Answering from Tables;

D O I：

10.1145/3430984.3430992

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present the designs of a system that is targeted at automated data curation from tables appearing in large documents like reports, technical articles etc.. There is an increasing demand for applications that can support question answering over tables. It can also be used to automatically fill-up data-sheets in a regular fashion. The task is complex since there are no fixed formats for these tables. Inferring table structure and extracting tabular data are known to be difficult problems. In this paper we present methods to retrieve tabular data from approximately inferred structures. We present QuATab, an end-to-end system that can do Q&A from tables contained in documents.

引用

页码：400 / 404

页数：5

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