Shallow semantic parsing for spoken language understanding

Most Spoken Dialog Systems are based on speech grammars and frame/slot semantics. The semantic descriptions of input utterances are usually defined ad-hoc with no ability to generalize beyond the target application domain or to learn from annotated corpora. The approach we propose in this paper exploits machine learning of frame semantics, borrowing its theoretical model from computational linguistics. While traditional automatic Semantic Role Labeling approaches on written texts may not perform as well on spoken dialogs, we show successful experiments on such porting. Hence, we design and evaluate automatic FrameNet-based parsers both for English written texts and for Italian dialog utterances. The results show that disfluencies of dialog data do not severely hurt performance. Also, a small set of FrameNet-like manual annotations is enough for realizing accurate Semantic Role Labeling on the target domains of typical Dialog Systems.

Verb subcategorization kernels for automatic semantic labeling

Recently, many researches in natural language learning have considered the representation of complex linguistic phenomena by means of structural kernels. In particular, tree kernels have been used to represent verbal subcategorization frame (SCF) information for predicate argument classification. As the SCF is a relevant clue to learn the relation between syntax and semantic, the classification algorithm accuracy was remarkable enhanced. In this article, we extend such work by studying the impact of the SCF tree kernel on both PropBank and FrameNet semantic roles. The experiments with Support Vector Machines (SVMs) confirm a strong link between the SCF and the semantics of the verbal predicates as well as the benefit of using kernels in diverse and complex test conditions, e.g. classification of unseen verbs.

Syntactic and semantic structure for opinion expression detection

We demonstrate that relational features derived from dependency-syntactic and semantic role structures are useful for the task of detecting opinionated expressions in natural-language text, significantly improving over conventional models based on sequence labeling with local features. These features allow us to model the way opinionated expressions interact in a sentence over arbitrary distances. While the relational features make the prediction task more computationally expensive, we show that it can be tackled effectively by using a reranker. We evaluate a number of machine learning approaches for the reranker, and the best model results in a 10-point absolute improvement in soft recall on the MPQA corpus, while decreasing precision only slightly.