Shallow Parsing of a Tennis Game from Audio Events
This paper proposes a method to infer the syn tactical units of a sports game (tennis) from a stream of game events.We assume that we are given a sequence of events within the game (examples of events are serve rally score announcement etc.),with their durations,and our goal is to segment them into units that are meaningful for the game,such as a point .Such a segmentation is essential for understanding the way that the events relate to each other,and hence for inferring automatically the structure of the game.We use a multi-gram based technique to segment the event steam into variable-length sequences by estimating the optimal (maximum-likelihood) segmentation using the Viterbi algorithm.We then make use of some extra contextual information,namely the time gap between two adjacent match events,which is in itself a reasonable indicator of segmentation.By integrating this feature into the multi-gram segmentation,we considerably enhance segmentation performance.The results show that our approach is an effective way to parse a tennis game from a stream of events with minimal human intervention.
Shallow parsing variable-length unit segmentation game learning
Qiang Huang Stephen Cox
School of Computing Sciences University of East Anglia Norwich,UK
国际会议
秦皇岛
英文
4-7
2010-11-05（万方平台首次上网日期，不代表论文的发表时间）