Exploring Adaptive Window Sizes for Entity Retrieval

Abstract

With the continuous attention of modern search engines to retrieve entities and not just documents for any given query, we introduce a new method for enhancing the entity-ranking task. An entity-ranking task is concerned with retrieving a ranked list of entities as a response to a specific query. Some successful models used the idea of association discovery in a window of text, rather than in the whole document. However, these studies considered only fixed window sizes. This work proposes a way of generating an adaptive window size for each document by utilising some of the document features. These features include document length, average sentence length, number of entities in the document, and the readability index. Experimental results show a positive effect once taking these document features into consideration when determining window size.