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Title:Character language models for generalization of multilingual named entity recognition
Author(s):Yu, Xiaodong
Advisor(s):Roth, Dan
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Character Language Models
Named Entity Recognition
Generalization
Multilingual
Multilingual Named Entity Recognition
NER
Abstract:State-of-the-art Named Entity Recognition (NER) models usually achieve high performance on entities that they have seen in training data, but a significantly lower performance on unseen entities. This is one of the key reasons in performance degradation observed when NER models are evaluated on new domains. Motivated by this observation, quantified for the first time in this thesis, we study an improved, multi-domain and multi-lingual, capability for identifying \what is a name". Character-level patterns have been widely used as features in English Named Entity Recognition (NER) systems. However, to date there has been no direct investigation of the inherent differences between name and non-name tokens in text, nor whether this property holds across multiple languages. The key contribution of this thesis is to develop a Character-level Language Model (CLM) that, as we show, allow us to better learn \what is a name". We analyze the capabilities of corpus-agnostic Character-level Language Models (CLMs) in the binary task of distinguishing name tokens from non-name tokens and demonstrate that CLMs provide a simple yet powerful model for capturing these differences. Specifically, we show that it can identify named entity tokens in a diverse set of languages at close to the performance of full NER systems. Moreover, by adding very simple CLM-based features we can significantly improve the performance of an o -the-shelf NER system for multiple languages.
Issue Date:2019-04-25
Type:Text
URI:http://hdl.handle.net/2142/104934
Rights Information:Copyright 2019 Xiaodong Yu
Date Available in IDEALS:2019-08-23
Date Deposited:2019-05


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