We propose a novel probabilistic method based on the Hidden Markov Model (HMM) to learn the structure of a Latent Variable Model (LVM) for query language modeling. In the proposed...
We describe a method for applying parsimonious language models to re-estimate the term probabilities assigned by relevance models. We apply our method to six topic sets from test ...
Edgar Meij, Wouter Weerkamp, Krisztian Balog, Maar...
We describe a open-domain information extraction method for extracting concept-instance pairs from an HTML corpus. Most earlier approaches to this problem rely on combining cluste...
Bhavana Bharat Dalvi, William W. Cohen, Jamie Call...
We propose a new multilevel framework for large-scale placement called MAPLE that respects utilization constraints, handles movable macros and guides the transition between global...
Myung-Chul Kim, Natarajan Viswanathan, Charles J. ...
We introduce a new normal form, called essential tuple normal form (ETNF), for relations in a relational database where the constraints are given by functional dependencies and jo...