Toward extracting information from public health statutes using text classification machine learning

Matthias Grabmair; Kevin D. Ashley; Rebecca Hwa; Patricia M. Sweeney

doi:10.3233/978-1-60750-981-3-73

Toward extracting information from public health statutes using text classification machine learning

Matthias Grabmair, Kevin D. Ashley, Rebecca Hwa, Patricia M. Sweeney

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

9 Scopus citations

Abstract

This paper presents preliminary results in extracting semantic information from US state public health legislative provisions using natural language processing techniques and machine learning classifiers. Challenges in the density and distribution of the data as well as the structure of the prediction task are described. Decision tree models trained on a unigram representation with TFIDF measures in most cases outperform the baselines by varying margins, leaving room for further improvement.

Original language	English
Title of host publication	Legal Knowledge and Information Systems - JURIX 2011
Subtitle of host publication	The Twenty-Fourth Annual Conference
Publisher	IOS Press BV
Pages	73-82
Number of pages	10
ISBN (Print)	9781607509806
DOIs	https://doi.org/10.3233/978-1-60750-981-3-73
State	Published - 2011
Externally published	Yes

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	235
ISSN (Print)	0922-6389
ISSN (Electronic)	1879-8314

Keywords

machine learning
natural language processing
semantic extraction

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/978-1-60750-981-3-73

Cite this

Grabmair, M., Ashley, K. D., Hwa, R., & Sweeney, P. M. (2011). Toward extracting information from public health statutes using text classification machine learning. In Legal Knowledge and Information Systems - JURIX 2011: The Twenty-Fourth Annual Conference (pp. 73-82). (Frontiers in Artificial Intelligence and Applications; Vol. 235). IOS Press BV. https://doi.org/10.3233/978-1-60750-981-3-73

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title = "Toward extracting information from public health statutes using text classification machine learning",

abstract = "This paper presents preliminary results in extracting semantic information from US state public health legislative provisions using natural language processing techniques and machine learning classifiers. Challenges in the density and distribution of the data as well as the structure of the prediction task are described. Decision tree models trained on a unigram representation with TFIDF measures in most cases outperform the baselines by varying margins, leaving room for further improvement.",

keywords = "machine learning, natural language processing, semantic extraction",

author = "Matthias Grabmair and Ashley, {Kevin D.} and Rebecca Hwa and Sweeney, {Patricia M.}",

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language = "English",

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pages = "73--82",

booktitle = "Legal Knowledge and Information Systems - JURIX 2011",

}

Grabmair, M, Ashley, KD, Hwa, R & Sweeney, PM 2011, Toward extracting information from public health statutes using text classification machine learning. in Legal Knowledge and Information Systems - JURIX 2011: The Twenty-Fourth Annual Conference. Frontiers in Artificial Intelligence and Applications, vol. 235, IOS Press BV, pp. 73-82. https://doi.org/10.3233/978-1-60750-981-3-73

Toward extracting information from public health statutes using text classification machine learning. / Grabmair, Matthias; Ashley, Kevin D.; Hwa, Rebecca et al.
Legal Knowledge and Information Systems - JURIX 2011: The Twenty-Fourth Annual Conference. IOS Press BV, 2011. p. 73-82 (Frontiers in Artificial Intelligence and Applications; Vol. 235).

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

TY - GEN

T1 - Toward extracting information from public health statutes using text classification machine learning

AU - Grabmair, Matthias

AU - Ashley, Kevin D.

AU - Hwa, Rebecca

AU - Sweeney, Patricia M.

PY - 2011

Y1 - 2011

N2 - This paper presents preliminary results in extracting semantic information from US state public health legislative provisions using natural language processing techniques and machine learning classifiers. Challenges in the density and distribution of the data as well as the structure of the prediction task are described. Decision tree models trained on a unigram representation with TFIDF measures in most cases outperform the baselines by varying margins, leaving room for further improvement.

AB - This paper presents preliminary results in extracting semantic information from US state public health legislative provisions using natural language processing techniques and machine learning classifiers. Challenges in the density and distribution of the data as well as the structure of the prediction task are described. Decision tree models trained on a unigram representation with TFIDF measures in most cases outperform the baselines by varying margins, leaving room for further improvement.

KW - machine learning

KW - natural language processing

KW - semantic extraction

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U2 - 10.3233/978-1-60750-981-3-73

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AN - SCOPUS:83655193521

SN - 9781607509806

T3 - Frontiers in Artificial Intelligence and Applications

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BT - Legal Knowledge and Information Systems - JURIX 2011

PB - IOS Press BV

ER -

Toward extracting information from public health statutes using text classification machine learning

Abstract

Publication series

Keywords

UN SDGs

Access to Document

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