Ensemble Learning of Named Entity Recognition Algorithms using Multilayer Perceptron for the Multilingual Web of Data

被引：3

作者：

Speck, Rene ^{[1
]}

Ngomo, Axel-Cyrille Ngonga ^{[2
]}

机构：

[1] Univ Leipzig, Data Sci Grp, Augustuspl 10, D-04109 Leipzig, Germany

[2] Univ Paderborn, Data Sci Grp, Pohlweg 51, D-33098 Paderborn, Germany

来源：

K-CAP 2017: PROCEEDINGS OF THE KNOWLEDGE CAPTURE CONFERENCE | 2017年

基金：

欧盟地平线“2020”;

关键词：

Named Entity Recognition; Ensemble Learning; Multilingual; Semantic Web;

D O I：

10.1145/3148011.3154471

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Implementing the multilingual Semantic Web vision requires transforming unstructured data in multiple languages from the Document Web into structured data for the multilingual Web of Data. We present the multilingual version of FOX, a knowledge extraction suite which supports this migration by providing named entity recognition based on ensemble learning for five languages. Our evaluation results show that our approach goes beyond the performance of existing named entity recognition systems on all five languages. In our best run, we outperform the state of the art by a gain of 32.38% F1-Score points on a Dutch dataset. More information and a demo can be found at http://fox.aksw.org as well as an extended version of the paper(1) descriping the evaluation in detail.

引用

页数：4

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