Shallow Parsing of a Tennis Game from Audio Events
Qiang Huang, Steven J. Cox · UEA Digital Repository (University of East Anglia) · 2010
This paper proposes a method to infer the syntactical 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 multigram 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. Keywords-Shallow parsing; variable-length unit; segmentation; game learning;