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Exploring adaptive window sizes for entity retrieval

Alarfaj, F and Kruschwitz, U and Fox, C (2014) Exploring adaptive window sizes for entity retrieval. In: UNSPECIFIED, ? - ?.

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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. © 2014 Springer International Publishing Switzerland.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Subjects: P Language and Literature > P Philology. Linguistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 04 Dec 2014 13:12
Last Modified: 23 Jan 2019 01:15

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