Interactive Feature Space Construction using Semantic Information

Specifying an appropriate feature space is an important aspect of achieving good performance when designing systems based upon learned classifiers. Effectively incorporating information regarding semantically related words into the feature space is known to produce robust, accurate classifiers and is one apparent motivation for efforts to automatically generate such resources. However, naive incorporation of this semantic information may result in poor performance due to increased ambiguity. To overcome this limitation, we introduce the interactive feature space construction protocol, where the learner identifies inadequate regions of the feature space and in coordination with a domain expert adds descriptiveness through existing semantic resources. We demonstrate effectiveness on an entity and relation extraction system including both performance improvements and robustness to reductions in annotated data.