 In this paper, we propose a four step solution to this problem. First, we generate candidate entity strings by segmenting queries in different ways. Second, we retrieve candidate entities by searching for these candidate entity stringsin Freebase. Third, we rank the candidate entities using language model based query likelihood scores. Finally, we group the entity annotations into interpretations. We also present both quantitative and qualitative evaluation of our methods based on 91 training, 500 validation and 1000 test queries. Our system achieved an F1 score of 0.42 on the set of validation queries, whereas the NULL baseline which returns no annotations for any query achieved an F1 score of 0.3. Similarly, on the test queries, our method achieved an F1 score of 0.36 and outperformed the NULL baseline which achieved an F1 score of 0.2.
