Engineering activities often produce considerable documentation as a by-product of the development process. Due to their complexity, technical analysts can benefit from text processing techniques able to identify concepts of interest and analyze deficiencies of the documents in an automated fashion. In practice, text sentences from the documentation are usually transformed to a vector space model, which is suitable for traditional machine learning classifiers. However, such transformations suffer from problems of synonyms and ambiguity that cause classification mistakes. For alleviating these problems, there has been a growing interest in the semantic enrichment of text. Unfortunately, using general-purpose thesaurus and encyclopedias to enrich technical documents belonging to a given domain (e.g. requirements engineering) often introduces noise and does not improve classification. In this work, we aim at boosting text classification by exploiting information about semantic roles. We have explored this approach when building a multi-label classifier for identifying special concepts, called domain actions, in textual software requirements. After evaluating various combinations of semantic roles and text classification algorithms, we found that this kind of semantically-enriched data leads to improvements of up to 18% in both precision and recall, when compared to non-enriched data. Our enrichment strategy based on semantic roles also allowed classifiers to reach acceptable accuracy levels with small training sets. Moreover, semantic roles outperformed Wikipedia- and WordNET-based enrichments, which failed to boost requirements classification with several techniques. These results drove the development of two requirements tools, which we successfully applied in the processing of textual use cases.