The student is expected to have a basic understanding of:

- the computational problem of mapping text to formal meaning representations affording logical reasoning;

- how this problem can be solved with a syntax-driven perspective, in a surface-compositional and incremental manner;

- how the same problem can be addressed through state-of-art empirical methods involving machine learning and probabilistic methods.

- how the two approaches can be combined.

The student is also expected to be able to apply the knowledge resources mentioned above to various applications in computational semantics including information extraction, question-answer systems, summarization, machine translation, conversational agents, among others.