Improving Related Entity Finding via Incorporating Homepages and Recognizing Fine-grained Entities

This paper describes experiments on the TREC entity track that studies retrieval of homepages representing entities relevant to a query. Many studies have focused on extracting entities that match the given coarse-grained types such as organizations, persons, locations by using a named entity recognizer, and employing language model techniques to calculate similarities between query and supporting snippets of entities from which entities are extracted to rank the entities. This paper proposes three improvements over baseline, i.e., 1) incorporating homepages of entities to supplement supporting snippets, 2) recognizing fine-grained named entities to filter out or negatively reward extracted entities that do not match the specified fine-grained types of entities such as a university, airline, author, and 3) adopting a dependency tree-based similarity method to improve language model techniques. Our experiments demonstrate that the proposed approaches can significantly improve performance, for instance, the absolute improvements o