Modeling Query Term Dependencies in Information Retrieval with Markov Random Fields

This paper develops a general, formal framework for modeling term dependencies via Markov random fields. The model allows for arbitrary text features to be incorporated as evidence. In particular, we make use of features based on occurrences of single terms, ordered phrases, and unordered phrases. We explore full independence, sequential dependence, and full dependence variants of the model. A novel approach is developed to train the model by directly maximizing mean average precision. Our results show that significant improvements are possible by modeling dependencies, especially on larger web collections.