@inproceedings{chen-etal-2010-language,
title = "A Language Approach to Modeling Human Behaviors",
author = "Chen, Peng-Wen and
Chennuru, Snehal Kumar and
Zhang, Ying",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/580_Paper.pdf",
abstract = "The modeling of human behavior becomes more and more important due to the increasing popularity of context-aware computing and people-centric mobile applications. Inspired by the principle of action-as-language, we propose that human ambulatory behavior shares similar properties as natural languages. In addition, by exploiting this similarity, we will be able to index, recognize, cluster, retrieve, and infer high-level semantic meanings of human behaviors via the use of natural language processing techniques. In this paper, we developed a Life Logger system to help build the behavior language corpus which supports our ''``Behavior as Language'''' research. The constructed behavior corpus shows Zipf's distribution over the frequency of vocabularies which is aligned with our ''``Behavior as Language'''' assumption. Our preliminary results of using smoothed n-gram language model for activity recognition achieved an average accuracy rate of 94{\%} in distinguishing among human ambulatory behaviors including walking, running, and cycling. This behavior-as-language corpus will enable researchers to study higher level human behavior based on the syntactic and semantic analysis of the corpus data.",
}
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<abstract>The modeling of human behavior becomes more and more important due to the increasing popularity of context-aware computing and people-centric mobile applications. Inspired by the principle of action-as-language, we propose that human ambulatory behavior shares similar properties as natural languages. In addition, by exploiting this similarity, we will be able to index, recognize, cluster, retrieve, and infer high-level semantic meanings of human behaviors via the use of natural language processing techniques. In this paper, we developed a Life Logger system to help build the behavior language corpus which supports our ”“Behavior as Language”” research. The constructed behavior corpus shows Zipf’s distribution over the frequency of vocabularies which is aligned with our ”“Behavior as Language”” assumption. Our preliminary results of using smoothed n-gram language model for activity recognition achieved an average accuracy rate of 94% in distinguishing among human ambulatory behaviors including walking, running, and cycling. This behavior-as-language corpus will enable researchers to study higher level human behavior based on the syntactic and semantic analysis of the corpus data.</abstract>
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%0 Conference Proceedings
%T A Language Approach to Modeling Human Behaviors
%A Chen, Peng-Wen
%A Chennuru, Snehal Kumar
%A Zhang, Ying
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F chen-etal-2010-language
%X The modeling of human behavior becomes more and more important due to the increasing popularity of context-aware computing and people-centric mobile applications. Inspired by the principle of action-as-language, we propose that human ambulatory behavior shares similar properties as natural languages. In addition, by exploiting this similarity, we will be able to index, recognize, cluster, retrieve, and infer high-level semantic meanings of human behaviors via the use of natural language processing techniques. In this paper, we developed a Life Logger system to help build the behavior language corpus which supports our ”“Behavior as Language”” research. The constructed behavior corpus shows Zipf’s distribution over the frequency of vocabularies which is aligned with our ”“Behavior as Language”” assumption. Our preliminary results of using smoothed n-gram language model for activity recognition achieved an average accuracy rate of 94% in distinguishing among human ambulatory behaviors including walking, running, and cycling. This behavior-as-language corpus will enable researchers to study higher level human behavior based on the syntactic and semantic analysis of the corpus data.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/580_Paper.pdf
Markdown (Informal)
[A Language Approach to Modeling Human Behaviors](http://www.lrec-conf.org/proceedings/lrec2010/pdf/580_Paper.pdf) (Chen et al., LREC 2010)
ACL
- Peng-Wen Chen, Snehal Kumar Chennuru, and Ying Zhang. 2010. A Language Approach to Modeling Human Behaviors. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).