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Erschienen in:
Turkish and Its Challenges for Language and Speech Processing Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

6. Turkish Named-Entity Recognition

verfasst von : Reyyan Yeniterzi, Gökhan Tür, Kemal Oflazer

Erschienen in: Turkish Natural Language Processing

Verlag: Springer International Publishing

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Abstract

Named-entity recognition is an important task for many other natural language processing tasks and applications such as information extraction, question answering, sentiment analysis, machine translation, etc. Over the last decades named-entity recognition for Turkish has attracted significant attention both in terms of systems development and resource development. After a brief description of the general named-entity recognition task, this chapter presents a comprehensive overview of the work on Turkish named-entity recognition along with the data resources various research efforts have built.

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Vorheriges Kapitel Turkish Speech Recognition
Nächstes Kapitel Dependency Parsing of Turkish
Fußnoten
1
Note that any suffixes on the last word of a named-entity is split as a separate token.
 
2
The evaluation scripts from the CONLL 2000 shared task can be found at www.​github.​com/​newsreader/​evaluation/​tree/​master/​nerc-evaluation (Accessed on Sept. 14, 2017).
 
3
The entity type counts are different in these studies due to either using different subsets or counting multiple token entities as one or not.
 
4
A local grammar is “a way of describing the syntactic behavior of groups of individual elements, which are related but whose similarities cannot be easily expressed using phrase structure rules” (Mason 2004).
 
5
The data collection used in this study is not exactly the same with data used in Küçük and Yazıcı (2009a,b).
 
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Metadaten
Titel
Turkish Named-Entity Recognition
verfasst von
Reyyan Yeniterzi
Gökhan Tür
Kemal Oflazer
Copyright-Jahr
2018
Buch
Turkish Natural Language Processing

Print ISBN: 978-3-319-90163-3

Electronic ISBN: 978-3-319-90165-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-90165-7_6

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