Liu, Yang

Permanent URI for this collectionhttps://hdl.handle.net/10735.1/4983

Yang Liu serves as an Associate Professor in the Computer Science department. She is also a member of the faculty in the Human Language Technology Research Institute and the Center for Robust Speech Systems. Her research interests include:

  • Natural language processing (summarization, sentiment analysis, information extraction, classification)
  • Speech recognition and understanding, spoken language processing
  • Speech and language disorder, clinical applications
  • Social media analysis
  • Machine learning in language processing

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Recent Submissions

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    Extractive Meeting Summarization Through Speaker Zone Detection
    (International Speech and Communication Association) Bokaei, M. H.; Sameti, H.; Liu, Yang; Liu, Yang
    In this paper we investigate the role of discourse analysis in extractive meeting summarization task. Specifically our proposed method comprises of two distinct steps. First we use a meeting segmentation algorithm in order to detect various functional parts of the input meeting. Afterwards, a two level scoring mechanism in a graph-based framework is used to score each dialogue act in order to extract the most valuable ones and include them in the extracted summary. We evaluate our proposed method on AMI and ICSI corpora and compare it with other state-of-the-art graph based algorithms according to various evaluation metrics. The experimental results show that our algorithm outperforms the other state-of-the-art ones according to most of the metrics and on both datasets.
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    Extractive Meeting Summarization Through Speaker Zone Detection
    (International Speech and Communication Association) Bokaei, M. H.; Sameti, H.; Liu, Yang; Liu, Yang
    In this paper we investigate the role of discourse analysis in extractive meeting summarization task. Specifically our proposed method comprises of two distinct steps. First we use a meeting segmentation algorithm in order to detect various functional parts of the input meeting. Afterwards, a two level scoring mechanism in a graph-based framework is used to score each dialogue act in order to extract the most valuable ones and include them in the extracted summary. We evaluate our proposed method on AMI and ICSI corpora and compare it with other state-of-the-art graph based algorithms according to various evaluation metrics. The experimental results show that our algorithm outperforms the other state-of-the-art ones according to most of the metrics and on both datasets.

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