Enhanced latent semantic indexing using cosine similarity measures for medical application

Joint Authors

al-Anzi, Fawwaz
Abu Zaynah, Dia

Source

The International Arab Journal of Information Technology

Issue

Vol. 17, Issue 5 (30 Sep. 2020), pp.742-749, 8 p.

Publisher

Zarqa University Deanship of Scientific Research

Publication Date

2020-09-30

Country of Publication

Jordan

No. of Pages

8

Main Subjects

Information Technology and Computer Science

Abstract EN

The Vector Space Model (VSM) is widely used in data mining and Information Retrieval (IR) systems as a common document representation model.

However, there are some challenges to this technique such as high dimensional space and semantic looseness of the representation.

Consequently, the Latent Semantic Indexing (LSI) was suggested to reduce the feature dimensions and to generate semantic rich features that can represent conceptual term-document associations.

In fact, LSI has been effectively employed in search engines and many other Natural Language Processing (NLP) applications.

Researchers thereby promote endless effort seeking for better performance.

In this paper, we propose an innovative method that can be used in search engines to find better matched contents of the retrieving documents.

The proposed method introduces a new extension for the LSI technique based on the cosine similarity measures.

The performance evaluation was carried out using an Arabic language data collection that contains 800 medical related documents, with more than 47,222 unique words.

The proposed method was assessed using a small testing set that contains five medical keywords.

The results show that the performance of the proposed method is superior when compared to the standard LSI.

American Psychological Association (APA)

al-Anzi, Fawwaz& Abu Zaynah, Dia. 2020. Enhanced latent semantic indexing using cosine similarity measures for medical application. The International Arab Journal of Information Technology،Vol. 17, no. 5, pp.742-749.
https://search.emarefa.net/detail/BIM-1439755

Modern Language Association (MLA)

al-Anzi, Fawwaz& Abu Zaynah, Dia. Enhanced latent semantic indexing using cosine similarity measures for medical application. The International Arab Journal of Information Technology Vol. 17, no. 5 (Sep. 2020), pp.742-749.
https://search.emarefa.net/detail/BIM-1439755

American Medical Association (AMA)

al-Anzi, Fawwaz& Abu Zaynah, Dia. Enhanced latent semantic indexing using cosine similarity measures for medical application. The International Arab Journal of Information Technology. 2020. Vol. 17, no. 5, pp.742-749.
https://search.emarefa.net/detail/BIM-1439755

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 748-749

Record ID

BIM-1439755