Semantic Understanding of Professional Soccer Commentaries
This work addresses semantic understanding for soccer commentary analysis, but it is incremental as it applies existing ideas to a new domain with specific gains.
The paper tackles the problem of semantic parsing by learning correspondences between complex sentences and event sets, using a discriminative similarity and ranking approach to discover macro-events, and shows significant outperformance over state-of-the-art methods on a novel dataset of professional soccer commentaries.
This paper presents a novel approach to the problem of semantic parsing via learning the correspondences between complex sentences and rich sets of events. Our main intuition is that correct correspondences tend to occur more frequently. Our model benefits from a discriminative notion of similarity to learn the correspondence between sentence and an event and a ranking machinery that scores the popularity of each correspondence. Our method can discover a group of events (called macro-events) that best describes a sentence. We evaluate our method on our novel dataset of professional soccer commentaries. The empirical results show that our method significantly outperforms the state-of-theart.