Specifying an appropriate feature space is an important aspect of achieving good perfor- mance when designing systems based upon learned classifiers. Effectively incorporat- ing information regarding semantically related words into the feature space is known to pro- duce robust, accurate classifiers and is one ap- parent motivation for efforts to automatically generate such resources. However, naive in- corporation of this semantic information may result in poor performance due to increased ambiguity. To overcome this limitation, we introduce the interactive feature space con- struction protocol, where the learner identi- fies inadequate regions of the feature space and in coordination with a domain expert adds descriptiveness through existing semantic re- sources. We demonstrate effective...

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