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Erschienen in:
Grammatical Annotation of Historical Portuguese: Generating a Corpus-Based Diachronic Dictionary Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Neural Networks for Featureless Named Entity Recognition in Czech

verfasst von : Jana Straková, Milan Straka, Jan Hajič

Erschienen in: Text, Speech, and Dialogue

Verlag: Springer International Publishing

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Abstract

We present a completely featureless, language agnostic named entity recognition system. Following recent advances in artificial neural network research, the recognizer employs parametric rectified linear units (PReLU), word embeddings and character-level embeddings based on gated linear units (GRU). Without any feature engineering, only with surface forms, lemmas and tags as input, the network achieves excellent results in Czech NER and surpasses the current state of the art of previously published Czech NER systems, which use manually designed rule-based orthographic classification features. Furthermore, the neural network achieves robust results even when only surface forms are available as input. In addition, the proposed neural network can use the manually designed rule-based orthographic classification features and in such combination, it exceeds the current state of the art by a wide margin.

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Vorheriges Kapitel Topic Modeling over Text Streams from Social Media
Nächstes Kapitel SubGram: Extending Skip-Gram Word Representation with Substrings
Fußnoten
4
Our system learns and predicts only outermost entities and is thus penalized for every misted nested named entity during evaluation.
 
5
We used the following options: -cbow 0 -window 5 -negative 5 -iter 1.
 
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Metadaten
Titel
Neural Networks for Featureless Named Entity Recognition in Czech
verfasst von
Jana Straková
Milan Straka
Jan Hajič
Copyright-Jahr
2016
Buch
Text, Speech, and Dialogue

Print ISBN: 978-3-319-45509-9

Electronic ISBN: 978-3-319-45510-5

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-45510-5_20