阅读字里行间:消除推文地理位置歧义的机器学习方法

Sunshin Lee, M. Farag, Tarek Kanan, E. Fox
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引用次数: 19

摘要

本文描述了一种机器学习(ML)方法,用于提取命名实体,并基于这些命名实体和相关内容消除推文的位置歧义。我们对推文进行了实验(例如,关于坑洼的推文),并发现使用ML算法和斯坦福NER在消除推文位置歧义方面有显着改善。添加由我们的分类器预测的状态信息,可以将明确地找到州级地理位置的可能性提高多达80%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Read between the lines: A Machine Learning Approach for Disambiguating the Geo-location of Tweets
This paper describes a Machine Learning (ML) approach for extracting named entities and disambiguating the location of tweets based on those named entities and related content. We conducted experiments with tweets (e.g., about potholes), and found significant improvement in disambiguating tweet locations using a ML algorithm along with the Stanford NER. Adding state information predicted by our classifiers increases the possibility to find the state-level geo-location unambiguously by up to 80%.
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