Domain Named Entity Recognition Method Based on Skip-gram Model

被引：0

作者：

Feng Yan-hong ^{[1
]}

Yu Hong ^{[1
]}

Sun Geng ^{[1
]}

Yu Xun-ran ^{[2
]}

机构：

[1] Dalian Ocean Univ, Coll Informat Engn, Dalian, Peoples R China

[2] Univ Int Business & Econ, Sch Int Trade & Econ, Beijing, Peoples R China

来源：

PROCEEDINGS FIRST INTERNATIONAL CONFERENCE ON ELECTRONICS INSTRUMENTATION & INFORMATION SYSTEMS (EIIS 2017) | 2017年

关键词：

domain named entity recognition; Skip-gram; semantic meaning; word embedding; domain characteristics;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Traditional domain named entity recognition (NER) methods mainly depended on manual features and were implemented by machine learning methods. These features have no capability to express semantic meaning and these methods are very sensitive for artificial features. To resolve these problems, a method based on Skip-gram model is proposed in this paper. In this method, using word embedding with semantic meaning as features, named entity recognition problem is straightly modeled as Skip-gram model, so it achieves end-to-end solution. Domain characteristics are integrated into this model for further improvement in result. The experiment is carried on Sogou and domain corpus. It shows that the proposed method can improve Recall, Precision and F measure of domain named entity recognition.

引用

页码：510 / 514

页数：5