Difference between revisions of "2017 LightweightMultilingualEntityEx"

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(Tag: continuous replacement)
m (Text replacement - " �n" to " fin")
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the cosine distance between a candidate entity's embedding
 
the cosine distance between a candidate entity's embedding
 
and the entity embeddings from the substrings of the input
 
and the entity embeddings from the substrings of the input
string, and including those as part of the �nal probability
+
string, and including those as part of the final probability
 
estimations. With this paper, we will release our implementation
 
estimations. With this paper, we will release our implementation
 
of FEL and the data packs (models) that it uses.
 
of FEL and the data packs (models) that it uses.
Line 508: Line 508:
 
a ranked list of labels for each node in V without modifying
 
a ranked list of labels for each node in V without modifying
 
the labels of nodes in V 0. We pick the highest ranked label
 
the labels of nodes in V 0. We pick the highest ranked label
for each node in V as the �nal candidate. The algorithm
+
for each node in V as the final candidate. The algorithm
 
complexity is O(jV jT) where T is number of iterations and
 
complexity is O(jV jT) where T is number of iterations and
 
jV j number of nodes in the graph. This makes the algorithm
 
jV j number of nodes in the graph. This makes the algorithm

Revision as of 23:24, 14 January 2020

Subject Headings: Text Analytics.

Notes

Cited By

Quotes

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.
AuthorAasish Pappu +, Roi Blanco +, Yashar Mehdad +, Amanda Stent + and Kapil Thadani +
doi10.1145/3018661.3018724 +
titleLightweight Multilingual Entity Extraction and Linking +
year2017 +