Abstract The aim of computational semantics is to capture the meaning of natural language expressions in representations suitable for performing inferences, in the service of understanding human language in written or spoken form. First‐order logic is a good starting point, both from the representation and inference point of view. But even if one makes the choice of first‐order logic as representation language, this is not enough: the computational semanticist needs to make further decisions on how to model events, tense, modal contexts, anaphora and plural entities. Semantic representations are usually built on top of a syntactic analysis, using unification, techniques from the lambda‐calculus or linear logic, to do the book‐keeping of variable naming. Inference has many potential applications in computational semantics. One way to implement inference is using algorithms from automated deduction dedicated to first‐order logic, such as theorem proving and model building. Theorem proving can help in finding contradictions or checking for new information. Finite model building can be seen as a complementary inference task to theorem proving, and it often makes sense to use both procedures in parallel. The models produced by model generators for texts not only show that the text is contradiction‐free; they also can be used for disambiguation tasks and linking interpretation with the real world. To make interesting inferences, often additional background knowledge is required (not expressed in the analysed text or speech parts). This can be derived (and turned into first‐order logic) from raw text, semi‐structured databases or large‐scale lexical databases such as WordNet. Promising future research directions of computational semantics are investigating alternative representation and inference methods (using weaker variants of first‐order logic, reasoning with defaults), and developing evaluation methods measuring the semantic adequacy of systems and formalisms.