Sentence retrieval for entity list extraction with a seed, context, and topic

Abstract

We present a variation of the corpus-based entity set expansion and entity list completion task. A user-specified query and a sentence containing one seed entity are the input to the task. The output is a list of sentences that contain other instances of the entity class indicated by the input. We construct a semantic query expansion model that leverages topical context around the seed entity and scores sentences. The proposed model finds 46% of the target entity class by retrieving 20 sentences on average. It achieves 16% improvement over BM25 in terms of recall@20.