Conditional models for detecting lambda-functions in a spoken language understanding system

In this paper, methods are proposed for hypothesizing lambda-expressions of referred objects in telephone dialogues. Relations between words, semantic constituents and their composition into lambda-expressions are modeled by conditional random fields (CRF), in which functions integrate manually derived template patterns with words and concept hypotheses. Substantial error reductions are obtained using these functions instead of just using words and concept n-grams. Manually derived patterns and models appear to be very useful and not difficult to obtain by generalizing significant examples fetched by the presence of specific semantic constituents.