@inproceedings{laur-etal-2020-estnltk,
title = "{E}st{NLTK} 1.6: Remastered {E}stonian {NLP} Pipeline",
author = {Laur, Sven and
Orasmaa, Siim and
S{\"a}rg, Dage and
Tammo, Paul},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.884/",
pages = "7152--7160",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "The goal of the EstNLTK Python library is to provide a unified programming interface for natural language processing in Estonian. As such, previous versions of the library have been immensely successful both in academic and industrial circles. However, they also contained serious structural limitations {--} it was hard to add new components and there was a lack of fine-grained control needed for back-end programming. These issues have been explicitly addressed in the EstNLTK library while preserving the intuitive interface for novices. We have remastered the basic NLP pipeline by adding many data cleaning steps that are necessary for analyzing real-life texts, and state of the art components for morphological analysis and fact extraction. Our evaluation on unlabelled data shows that the remastered basic NLP pipeline outperforms both the previous version of the toolkit, as well as neural models of StanfordNLP. In addition, EstNLTK contains a new interface for storing, processing and querying text objects in Postgres database which greatly simplifies processing of large text collections. EstNLTK is freely available under the GNU GPL version 2 license, which is standard for academic software."
}
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<abstract>The goal of the EstNLTK Python library is to provide a unified programming interface for natural language processing in Estonian. As such, previous versions of the library have been immensely successful both in academic and industrial circles. However, they also contained serious structural limitations – it was hard to add new components and there was a lack of fine-grained control needed for back-end programming. These issues have been explicitly addressed in the EstNLTK library while preserving the intuitive interface for novices. We have remastered the basic NLP pipeline by adding many data cleaning steps that are necessary for analyzing real-life texts, and state of the art components for morphological analysis and fact extraction. Our evaluation on unlabelled data shows that the remastered basic NLP pipeline outperforms both the previous version of the toolkit, as well as neural models of StanfordNLP. In addition, EstNLTK contains a new interface for storing, processing and querying text objects in Postgres database which greatly simplifies processing of large text collections. EstNLTK is freely available under the GNU GPL version 2 license, which is standard for academic software.</abstract>
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%0 Conference Proceedings
%T EstNLTK 1.6: Remastered Estonian NLP Pipeline
%A Laur, Sven
%A Orasmaa, Siim
%A Särg, Dage
%A Tammo, Paul
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G eng
%F laur-etal-2020-estnltk
%X The goal of the EstNLTK Python library is to provide a unified programming interface for natural language processing in Estonian. As such, previous versions of the library have been immensely successful both in academic and industrial circles. However, they also contained serious structural limitations – it was hard to add new components and there was a lack of fine-grained control needed for back-end programming. These issues have been explicitly addressed in the EstNLTK library while preserving the intuitive interface for novices. We have remastered the basic NLP pipeline by adding many data cleaning steps that are necessary for analyzing real-life texts, and state of the art components for morphological analysis and fact extraction. Our evaluation on unlabelled data shows that the remastered basic NLP pipeline outperforms both the previous version of the toolkit, as well as neural models of StanfordNLP. In addition, EstNLTK contains a new interface for storing, processing and querying text objects in Postgres database which greatly simplifies processing of large text collections. EstNLTK is freely available under the GNU GPL version 2 license, which is standard for academic software.
%U https://aclanthology.org/2020.lrec-1.884/
%P 7152-7160
Markdown (Informal)
[EstNLTK 1.6: Remastered Estonian NLP Pipeline](https://aclanthology.org/2020.lrec-1.884/) (Laur et al., LREC 2020)
ACL
- Sven Laur, Siim Orasmaa, Dage Särg, and Paul Tammo. 2020. EstNLTK 1.6: Remastered Estonian NLP Pipeline. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 7152–7160, Marseille, France. European Language Resources Association.