Efficient Batch Top-k Search for Dictionary-based Entity Recognition

We consider the problem of speeding up Entity Recognition systems that exploit existing large databases of structured entities to improve extraction accuracy. These systems require the computation of the maximum similarity scores of several overlapping segments of the input text with the entity database. We formulate a Batch-Top-K problem with the goal of sharing computations across overlapping segments. Our proposed algorithm performs a factor of three faster than independent Top-K queries and only a factor of two slower than an unachievable lower bound on total cost. We then propose a novel modification of the popular Viterbi algorithm for recognizing entities so as to work with easily computable bounds on match scores, thereby reducing the total inference time by a factor of eight compared to stateof- the-art methods.