In a previous question answering study, we identified nine semantic-relationship types, including synonyms, hypernyms, word chains, and holonyms, that exist between terms in Text Retrieval Conference queries and those in their supporting sentences in the Advanced Question Answering for Intelligence (Graff, 2002) corpus. The most frequently occurring relationship type was the hypernym (e.g., Katherine Hepburn is an actress). The aim of the present work, therefore, was to develop a method for determining a person's occupation from syntactic data in a text corpus. First, in the P-System, we compared predicate–argument data involving a proper name for different occupations using Okapi's BM25 weighting algorithm. When classifying actors and using sufficiently frequent names, an accuracy of 0.955 was attained. For evaluation purposes, we also implemented a standard apposition-based classifier (A-System). This performs well, but only if a particular name happens to appear in apposition with the corresponding occupation. Last, we created a hybrid (H-System) which combines the strengths of P with those of A. Using data with a minimum of 100 predicate–argument pairs, H performed best with an overall lenient accuracy of 0.750 while A and P scored 0.615 and 0.656, respectively. We therefore conclude that a hybrid approach combining information from different sources is the best way to predict occupations.