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Authors: Dapeng Zhang and Bernhard Nebel

Affiliation: University of Freiburg, Germany

Keyword(s): Conditional random fields, CRF queue, Feature induction.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems ; Uncertainty in AI

Abstract: Conditional Random Fields (CRFs) is a probabilistic framework for labeling sequential data. Several approaches were developed to automatically induce features for CRFs. They have been successfully applied in real-world applications, e.g. in natural language processing. The work described in this paper was originally motivated by processing the sequence data of table soccer games. As labeling such data is very time consuming, we developed a sequence generator (simulation), which creates an extra phase to explore several basic issues of the feature induction of linear-chain CRFs. First, we generated data sets with different configurations of overlapped and conjunct atomic features, and discussed how these factors affect the induction. Then, a reduction step was integrated into the induction which maintained the prediction accuracy and saved the computational power. Finally, we developed an approach which consists of a queue of CRFs. The experiments show that the CRF queue achieves bett er results on the data sets in all the configurations. (More)

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Paper citation in several formats:
Zhang, D. and Nebel, B. (2011). FEATURE INDUCTION OF LINEAR-CHAIN CONDITIONAL RANDOM FIELDS - A Study based on a Simulation. In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-8425-40-9; ISSN 2184-433X, SciTePress, pages 230-235. DOI: 10.5220/0003144102300235

@conference{icaart11,
author={Dapeng Zhang. and Bernhard Nebel.},
title={FEATURE INDUCTION OF LINEAR-CHAIN CONDITIONAL RANDOM FIELDS - A Study based on a Simulation},
booktitle={Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2011},
pages={230-235},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003144102300235},
isbn={978-989-8425-40-9},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - FEATURE INDUCTION OF LINEAR-CHAIN CONDITIONAL RANDOM FIELDS - A Study based on a Simulation
SN - 978-989-8425-40-9
IS - 2184-433X
AU - Zhang, D.
AU - Nebel, B.
PY - 2011
SP - 230
EP - 235
DO - 10.5220/0003144102300235
PB - SciTePress