An Ensemble method for Content Selection for Data-to-text Systems

Dimitra Gkatzia; Helen Hastie

An Ensemble method for Content Selection for Data-to-text Systems

Research output: Contribution to conference › Paper › peer-review

Abstract / Description of output

We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label classification (MLC) problem, which takes as input time-series data (students' learning data) and outputs a summary of these data (feedback). Unlike previous work, this method considers all data simultaneously using ensembles of classifiers, and therefore, it achieves higher accuracy and F- score compared to meaningful baselines.

Original language	English
Publication status	Published - 6 Mar 2015
Event	1st Workshop on Data-to-text Generation - Edinburgh, United Kingdom Duration: 6 Mar 2015 → … Conference number: 1 https://www.aclweb.org/portal/content/1st-workshop-data-text-generation

Workshop

Workshop	1st Workshop on Data-to-text Generation
Country/Territory	United Kingdom
City	Edinburgh
Period	6/03/15 → …
Internet address	https://www.aclweb.org/portal/content/1st-workshop-data-text-generation

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https://arxiv.org/abs/1506.02922Licence: All Rights Reserved

Cite this

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title = "An Ensemble method for Content Selection for Data-to-text Systems",

abstract = "We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label classification (MLC) problem, which takes as input time-series data (students' learning data) and outputs a summary of these data (feedback). Unlike previous work, this method considers all data simultaneously using ensembles of classifiers, and therefore, it achieves higher accuracy and F- score compared to meaningful baselines.",

author = "Dimitra Gkatzia and Helen Hastie",

year = "2015",

month = mar,

day = "6",

language = "English",

note = "1st Workshop on Data-to-text Generation<br/> ; Conference date: 06-03-2015",

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TY - CONF

T1 - An Ensemble method for Content Selection for Data-to-text Systems

AU - Gkatzia, Dimitra

AU - Hastie, Helen

N1 - Conference code: 1

PY - 2015/3/6

Y1 - 2015/3/6

N2 - We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label classification (MLC) problem, which takes as input time-series data (students' learning data) and outputs a summary of these data (feedback). Unlike previous work, this method considers all data simultaneously using ensembles of classifiers, and therefore, it achieves higher accuracy and F- score compared to meaningful baselines.

AB - We present a novel approach for automatic report generation from time-series data, in the context of student feedback generation. Our proposed methodology treats content selection as a multi-label classification (MLC) problem, which takes as input time-series data (students' learning data) and outputs a summary of these data (feedback). Unlike previous work, this method considers all data simultaneously using ensembles of classifiers, and therefore, it achieves higher accuracy and F- score compared to meaningful baselines.

M3 - Paper

T2 - 1st Workshop on Data-to-text Generation<br/>

Y2 - 6 March 2015

ER -

An Ensemble method for Content Selection for Data-to-text Systems

Abstract / Description of output

Workshop

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Cite this