Relevance of semantic-enriched in information retrieval nodels

Kenan M Matawie ^[1] ; Sargon Hasso ^[2]
1. [1] University of Western Sydney
  
  University of Western Sydney
  
  Australia
2. [2] UL LLC, Northbrook
Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.), Dae-Jin Lee (ed. lit.), Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 370-373
Idioma: inglés
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Resumen
- Improving document relevance in Information Retrieval has been recently the focus of many research projects and papers. Such investigations and developments are very helpful and essential for everyday private and commercial decisions making process. The main parts of this improvement are the scoring models such as BM25 and the evaluation of the performance of these techniques such as the rank-based models, e.g. MAP, NDCG and RBP. Here we are focusing on the semantic enrichment of the documents using specialised dictionary that will improve the score and rank of the search results. This enrichment is analysed and presented using TREC data and utilizing Lucene full-text search library.