Meta-learning of Text Classification Tasks

被引：3

作者：

Madrid, Jorge G. ^{[1
]}

Jair Escalante, Hugo ^{[1
,2
]}

机构：

[1] Inst Nacl Astrofis Opt & Electr, Dept Comp Sci, Cholula 72840, Pue, Mexico

[2] IPN, Ctr Invest & Estudios Avanzados, Dept Comp Sci, Mexico City 07360, DF, Mexico

来源：

PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS (CIARP 2019) | 2019年 / 11896卷

关键词：

Meta-learning; Text classification; Meta-features;

D O I：

10.1007/978-3-030-33904-3_10

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A text mining characterization is proposed consisting of a set of meta-features, unlike previous meta-learning approaches, some of them are extracted directly from raw text. Such novel description is useful for comparing text mining tasks and study their differences. The problem of determining the task associated to a text classification dataset is introduced and approached with our characterization. Experimental results on a set of 81 corpora show that the proposed meta-features indeed allow to recognize tasks with acceptable performance using only a few meta-features.

引用

页码：107 / 119

页数：13

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