Multi-lingual geoparsing based on machine translation

被引：2

作者：

Chen, Xu ^{[1
,2
]}

Gelernter, Judith ^{[2
]}

Zhang, Han ^{[2
]}

Liu, Jin ^{[1
]}

机构：

[1] Wuhan Univ, State Key Lab Software Engn, Comp Sch, Wuhan, Hubei, Peoples R China

[2] Carnegie Mellon Univ, Sch Comp Sci, Language Technol Inst, Pittsburgh, PA 15213 USA

来源：

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2019年 / 96卷

基金：

中国国家自然科学基金;

关键词：

Named entities recognition; Location; Geoparse; Multi-lingual; Machine translation; Word Alignment; NAMED ENTITY RECOGNITION;

D O I：

10.1016/j.future.2017.07.057

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Our method for multi-lingual geoparsing uses monolingual tools and resources along with machine translation and alignment to return location words in many languages. Not only does our method save the time and cost of developing geoparsers for each language separately, but also it allows the possibility of a wide range of having a wide range of language capabilities within a single interface. We evaluated our method in our LanguageBridge prototype on location named entities using newswire, broadcast news and telephone conversations in English, Arabic and Chinese data from the Linguistic Data Consortium (LDC). Our results for geoparsing Chinese and Arabic text using our multi-lingual geoparsing method are comparable to our results for geoparsing English text with our English tools. Furthermore, our experiments using our tools on machine translation approach in accuracy results on results from the same data that was translated manually, further showing the robustness of locations to machine translation. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：667 / 677

页数：11

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