Semantic Parsing with Relaxed Hybrid Trees
Wei Lu · 2014
We propose a novel model for parsing natural language sentences into their for-mal semantic representations. The model is able to perform integrated lexicon ac-quisition and semantic parsing, mapping each atomic element in a complete seman-tic representation to a contiguous word sequence in the input sentence in a re-cursive manner, where certain overlap-pings amongst such word sequences are allowed. It defines distributions over the novel relaxed hybrid tree structures which jointly represent both sentences and se-mantics. Such structures allow tractable dynamic programming algorithms to be developed for efficient learning and decod-ing. Trained under a discriminative set-ting, our model is able to incorporate a rich set of features where certain unbounded long-distance dependencies can be cap-tured in a principled manner. We demon-strate through experiments that by exploit-ing a large collection of simple features, our model is shown to be competitive to previous works and achieves state-of-the-art performance on standard benchmark data across four different languages. The system and code can be downloaded from