2017 LightweightMultilingualEntityEx

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Subject Headings: Text Analytics.

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Abstract

Text analytics systems often rely heavily on detecting and linking entity mentions in documents to knowledge bases for downstream applications such as sentiment analysis, question answering and recommender systems. A major challenge for this task is to be able to accurately detect entities in new languages with limited labeled resources. In this paper we present an accurate and lightweight [1], multilingual named entity recognition (NER) and linking (NEL) system. The contributions of this paper are three-fold: 1) Lightweight named entity recognition with competitive accuracy; 2) Candidate entity retrieval that uses search click-log data and entity embeddings to achieve high precision with a low memory footprint; and 3) efficient entity disambiguation. Our system achieves state-of-the-art performance on TAC KBP 2013 multilingual data and on English AIDA CONLL data.


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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2017 LightweightMultilingualEntityExAasish Pappu
Roi Blanco
Yashar Mehdad
Amanda Stent
Kapil Thadani		Lightweight Multilingual Entity Extraction and Linking10.1145/3018661.30187242017
  1. ↑ By lightweight, we mean easily extensible to additional languages, with a low memory footprint, and fast.
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