Ensemble Learning for Named Entity Recognition

author: René Speck, Agile Knowledge Engineering and Semantic Web (AKSW), University of Leipzig
published: Dec. 19, 2014,   recorded: October 2014,   views: 47
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Description

A considerable portion of the information on the Web is still only available in unstructured form. Implementing the vision of the Semantic Web thus requires transforming this unstructured data into structured data. One key stepduringthisprocessistherecognitionofnamedentities.Previousworkssug- gest that ensemble learning can be used to improve the performance of named entity recognition tools. However, no comparison of the performance of existing supervised machine learning approaches on this task has been presented so far. Weaddressthisresearchgapbypresentingathoroughevaluationofnamedentity recognition based on ensemble learning. To this end, we combine four different state-of-the approaches by using 15 different algorithms for ensemble learning and evaluate their performace on five different datasets. Our results suggest that ensemblelearningcanreducetheerrorrateofstate-of-the-artnamedentityr ecognition systems by 40%, thereby leading to over 95% f-score in our best run.

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