Highly scalable text mining - Parallel tagging application

Firat Tekiner; Yoshimasa Tsuruoka; Jun'ichi Tsujii; Sophia Ananiadou

doi:10.1109/ICSCCW.2009.5379432

Highly scalable text mining - Parallel tagging application

Firat Tekiner, Yoshimasa Tsuruoka, Jun'ichi Tsujii, Sophia Ananiadou

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

There is an urgent need to develop new text mining solutions using High Performance Computing (HPC) and grid environments to tackle exponential growth in text data. Problem sizes are increasing by the day by addition of new text docments. The task of labelling sequence data such as part-of-speech (POS) tagging, chunking (shallow parsing) and named entity recognition is one of the most important tasks in Text Mining. Genia is a POS tagger which is specifically tuned for biomedical text. Genia is built with maximum entropy modelling and state of the art tagging algorithm. A Parallel version of genia tagger application has been implemented and performance has been compared on a number of different architectures. The focus has been particularly on scalability of the application. Scaling of 512 processors has been achieved and a method to scale to 10000 processors is proposed for massively parallel Text Mining applications. The parallel implementation of genia tagger is done using MPI for achieving portable code. ©2009 IEEE.

Original language	English
Title of host publication	ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control\|ICSCCW - Int. Conf. Soft Comput., Comput. Words Percept. Syst. Anal., Decis. Control
DOIs	https://doi.org/10.1109/ICSCCW.2009.5379432
Publication status	Published - 2009
Event	5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, ICSCCW 2009 - Famagusta Duration: 1 Jul 2009 → …

Conference

Conference	5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, ICSCCW 2009
City	Famagusta
Period	1/07/09 → …

Keywords

Component
HPC
Parallel computing
Parsing
Text mining

Access to Document

10.1109/ICSCCW.2009.5379432

Cite this

Tekiner, F., Tsuruoka, Y., Tsujii, J., & Ananiadou, S. (2009). Highly scalable text mining - Parallel tagging application. In ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control|ICSCCW - Int. Conf. Soft Comput., Comput. Words Percept. Syst. Anal., Decis. Control https://doi.org/10.1109/ICSCCW.2009.5379432

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title = "Highly scalable text mining - Parallel tagging application",

abstract = "There is an urgent need to develop new text mining solutions using High Performance Computing (HPC) and grid environments to tackle exponential growth in text data. Problem sizes are increasing by the day by addition of new text docments. The task of labelling sequence data such as part-of-speech (POS) tagging, chunking (shallow parsing) and named entity recognition is one of the most important tasks in Text Mining. Genia is a POS tagger which is specifically tuned for biomedical text. Genia is built with maximum entropy modelling and state of the art tagging algorithm. A Parallel version of genia tagger application has been implemented and performance has been compared on a number of different architectures. The focus has been particularly on scalability of the application. Scaling of 512 processors has been achieved and a method to scale to 10000 processors is proposed for massively parallel Text Mining applications. The parallel implementation of genia tagger is done using MPI for achieving portable code. {\textcopyright}2009 IEEE.",

keywords = "Component, HPC, Parallel computing, Parsing, Text mining",

author = "Firat Tekiner and Yoshimasa Tsuruoka and Jun'ichi Tsujii and Sophia Ananiadou",

year = "2009",

doi = "10.1109/ICSCCW.2009.5379432",

language = "English",

isbn = "9781424434282",

booktitle = "ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control|ICSCCW - Int. Conf. Soft Comput., Comput. Words Percept. Syst. Anal., Decis. Control",

note = "5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, ICSCCW 2009 ; Conference date: 01-07-2009",

}

Tekiner, F, Tsuruoka, Y, Tsujii, J & Ananiadou, S 2009, Highly scalable text mining - Parallel tagging application. in ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control|ICSCCW - Int. Conf. Soft Comput., Comput. Words Percept. Syst. Anal., Decis. Control. 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, ICSCCW 2009, Famagusta, 1/07/09. https://doi.org/10.1109/ICSCCW.2009.5379432

Highly scalable text mining - Parallel tagging application. / Tekiner, Firat; Tsuruoka, Yoshimasa; Tsujii, Jun'ichi et al.
ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control|ICSCCW - Int. Conf. Soft Comput., Comput. Words Percept. Syst. Anal., Decis. Control. 2009.

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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T1 - Highly scalable text mining - Parallel tagging application

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AU - Tsuruoka, Yoshimasa

AU - Tsujii, Jun'ichi

AU - Ananiadou, Sophia

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N2 - There is an urgent need to develop new text mining solutions using High Performance Computing (HPC) and grid environments to tackle exponential growth in text data. Problem sizes are increasing by the day by addition of new text docments. The task of labelling sequence data such as part-of-speech (POS) tagging, chunking (shallow parsing) and named entity recognition is one of the most important tasks in Text Mining. Genia is a POS tagger which is specifically tuned for biomedical text. Genia is built with maximum entropy modelling and state of the art tagging algorithm. A Parallel version of genia tagger application has been implemented and performance has been compared on a number of different architectures. The focus has been particularly on scalability of the application. Scaling of 512 processors has been achieved and a method to scale to 10000 processors is proposed for massively parallel Text Mining applications. The parallel implementation of genia tagger is done using MPI for achieving portable code. ©2009 IEEE.

AB - There is an urgent need to develop new text mining solutions using High Performance Computing (HPC) and grid environments to tackle exponential growth in text data. Problem sizes are increasing by the day by addition of new text docments. The task of labelling sequence data such as part-of-speech (POS) tagging, chunking (shallow parsing) and named entity recognition is one of the most important tasks in Text Mining. Genia is a POS tagger which is specifically tuned for biomedical text. Genia is built with maximum entropy modelling and state of the art tagging algorithm. A Parallel version of genia tagger application has been implemented and performance has been compared on a number of different architectures. The focus has been particularly on scalability of the application. Scaling of 512 processors has been achieved and a method to scale to 10000 processors is proposed for massively parallel Text Mining applications. The parallel implementation of genia tagger is done using MPI for achieving portable code. ©2009 IEEE.

KW - Component

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KW - Parsing

KW - Text mining

U2 - 10.1109/ICSCCW.2009.5379432

DO - 10.1109/ICSCCW.2009.5379432

M3 - Conference contribution

SN - 9781424434282

BT - ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control|ICSCCW - Int. Conf. Soft Comput., Comput. Words Percept. Syst. Anal., Decis. Control

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Tekiner F, Tsuruoka Y, Tsujii J , Ananiadou S. Highly scalable text mining - Parallel tagging application. In ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control|ICSCCW - Int. Conf. Soft Comput., Comput. Words Percept. Syst. Anal., Decis. Control. 2009 doi: 10.1109/ICSCCW.2009.5379432

Highly scalable text mining - Parallel tagging application

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Keywords

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