Memory-based Named Entity Recognition in Tweets

Antal Van den Bosch; Toine Bogers

Memory-based Named Entity Recognition in Tweets

Antal Van den Bosch
, Toine Bogers

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

Abstract

We present a memory-based named entity recognition system that participated in the MSM-2013 Concept Extraction Challenge. The system expands the training set of annotated tweets with part-of-speech tags and seedlist information, and then generates a sequential memory-based tagger comprised of separate modules for known and unknown words. Two taggers are trained: one on the original capitalized data, and one on a lowercased version of the training data. The intersection of named entities in the predictions of the two taggers is kept as the final output.

Original language	English
Title of host publication	MSM 2013 : Proceedings of the 3rd WWW Workshop on Making Sense of Microposts
Editors	Amparo Cano, Matthew Rowe, Milan Stankovic, Aba-Sah Dadzie
Number of pages	4
Publisher	CEUR Workshop Proceedings
Publication date	13 May 2013
Pages	40-43
Publication status	Published - 13 May 2013
Externally published	Yes
Event	3rd workshop on 'Making Sense of Microposts' - RIo de Janerio, Brazil Duration: 13 May 2013 → … http://oak.dcs.shef.ac.uk/msm2013/

Conference

Conference	3rd workshop on 'Making Sense of Microposts'
Country/Territory	Brazil
City	RIo de Janerio
Period	13/05/2013 → …
Internet address	http://oak.dcs.shef.ac.uk/msm2013/

Series	CEUR Workshop Proceedings
Volume	1019
ISSN	1613-0073

Keywords

memory-based named entity recognition
concept extraction
natural language processing
sequential tagger
annotated tweets

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@inproceedings{a546dac0dbeb4e39bb97e8efaf01fd17,

title = "Memory-based Named Entity Recognition in Tweets",

abstract = "We present a memory-based named entity recognition system that participated in the MSM-2013 Concept Extraction Challenge. The system expands the training set of annotated tweets with part-of-speech tags and seedlist information, and then generates a sequential memory-based tagger comprised of separate modules for known and unknown words. Two taggers are trained: one on the original capitalized data, and one on a lowercased version of the training data. The intersection of named entities in the predictions of the two taggers is kept as the final output.",

keywords = "memory-based named entity recognition, concept extraction, natural language processing, sequential tagger, annotated tweets, memory-based named entity recognition, concept extraction, natural language processing, sequential tagger, annotated tweets",

author = "\{Van den Bosch\}, Antal and Toine Bogers",

year = "2013",

month = may,

day = "13",

language = "English",

series = "CEUR Workshop Proceedings",

publisher = "CEUR Workshop Proceedings",

pages = "40--43",

editor = "Amparo Cano and Matthew Rowe and Milan Stankovic and Aba-Sah Dadzie",

booktitle = "MSM 2013",

note = "3rd workshop on 'Making Sense of Microposts', MSM2013 ; Conference date: 13-05-2013",

url = "http://oak.dcs.shef.ac.uk/msm2013/",

}

Van den Bosch, A & Bogers, T 2013, Memory-based Named Entity Recognition in Tweets. in A Cano, M Rowe, M Stankovic & A-S Dadzie (eds), MSM 2013: Proceedings of the 3rd WWW Workshop on Making Sense of Microposts. CEUR Workshop Proceedings, CEUR Workshop Proceedings, vol. 1019, pp. 40-43, 3rd workshop on 'Making Sense of Microposts', RIo de Janerio, Brazil, 13/05/2013. <http://oak.dcs.shef.ac.uk/msm2013/>

Memory-based Named Entity Recognition in Tweets. / Van den Bosch, Antal; Bogers, Toine.
MSM 2013: Proceedings of the 3rd WWW Workshop on Making Sense of Microposts. ed. / Amparo Cano; Matthew Rowe; Milan Stankovic; Aba-Sah Dadzie. CEUR Workshop Proceedings, 2013. p. 40-43 (CEUR Workshop Proceedings, Vol. 1019).

Research output: Conference Article in Proceeding or Book/Report chapter › Article in proceedings › Research › peer-review

TY - GEN

T1 - Memory-based Named Entity Recognition in Tweets

AU - Van den Bosch, Antal

AU - Bogers, Toine

PY - 2013/5/13

Y1 - 2013/5/13

N2 - We present a memory-based named entity recognition system that participated in the MSM-2013 Concept Extraction Challenge. The system expands the training set of annotated tweets with part-of-speech tags and seedlist information, and then generates a sequential memory-based tagger comprised of separate modules for known and unknown words. Two taggers are trained: one on the original capitalized data, and one on a lowercased version of the training data. The intersection of named entities in the predictions of the two taggers is kept as the final output.

AB - We present a memory-based named entity recognition system that participated in the MSM-2013 Concept Extraction Challenge. The system expands the training set of annotated tweets with part-of-speech tags and seedlist information, and then generates a sequential memory-based tagger comprised of separate modules for known and unknown words. Two taggers are trained: one on the original capitalized data, and one on a lowercased version of the training data. The intersection of named entities in the predictions of the two taggers is kept as the final output.

KW - memory-based named entity recognition

KW - concept extraction

KW - natural language processing

KW - sequential tagger

KW - annotated tweets

KW - memory-based named entity recognition

KW - concept extraction

KW - natural language processing

KW - sequential tagger

KW - annotated tweets

M3 - Article in proceedings

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BT - MSM 2013

A2 - Cano, Amparo

A2 - Rowe, Matthew

A2 - Stankovic, Milan

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PB - CEUR Workshop Proceedings

T2 - 3rd workshop on 'Making Sense of Microposts'

Y2 - 13 May 2013

ER -

Memory-based Named Entity Recognition in Tweets

Abstract

Conference

Keywords

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