@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 = "https://preview.aclanthology.org/jlcl-multiple-ingestion/L10-1399/",
    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."
}