Semantic Understanding 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 event. 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-the-art. .
The above figure shows examples of sentences in the commentaries with corresponding buckets of events. The correct correspondences
in each bucket are marked with arrows.