Nested Dolls: Towards Unsupervised Clustering of Web Tables

被引：0

作者：

Khan, Rituparna ^{[1
]}

Gubanov, Michael ^{[1
]}

机构：

[1] Florida State Univ, Dept Comp Sci, Tallahassee, FL 32306 USA

来源：

2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2018年

基金：

美国国家科学基金会;

关键词：

Web-search; Large-scale Data Management; Big Data; Data Fusion; Data Integration; Data Cleaning; Summarization; Human-Computer Interaction; Machine Learning; Natural Language Processing (NLP);

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Here we discuss our initial efforts towards unsupervised clustering of a large-scale Web tables dataset. We improve our previous approach of weakly-supervised clustering, where an operator would provide a few descriptive keywords to generate an entity-identifying classifier, which is applied to the corpora to form a cohesive entity-centric cluster [1]. Here, we make a next step towards fully unsupervised algorithm by automatically generating these descriptive keywords. These keywords then can be used to generate high-precision training data and train a classifier to form a cluster. Here, we describe and evaluate this new unsupervised keyword generation algorithm and apply it to a large-scale Web tables corpus to form initial small high-precision clusters.

引用

页码：5357 / 5359

页数：3

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