Title:
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PHARMACEUTICAL SEARCH ENGINE |
Author(s):
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Ghadeer Aldweik, Saadia Malik, Abrar Almuhammidi, Wejdan Alyoubi, Ahad Alsulami and Hind Al-oufi |
ISBN:
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978-989-8704-14-6 |
Editors:
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Piet Kommers, Boyan Bontchev and Pedro IsaĆas |
Year:
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2020 |
Edition:
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Single |
Keywords:
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Vertical Search Engine, Pharmaceutical, Classifier, BM25 Weighting |
Type:
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Full |
First Page:
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81 |
Last Page:
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88 |
Language:
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English |
Cover:
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Full Contents:
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click to dowload
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Paper Abstract:
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The vertical search engine searches in the text of specific domain. In this project, we built a pharmaceutical vertical search
engine using a supervised learning classifier, Rocchio, to classify documents into two different classes; one pharmaceutical
and another computer science. For learning of the classifier, small document collection is created. It is evaluated using
abstracts from 86 research papers and accuracy yields 90% results. An inverted index is built containing terms from selected
pharmaceutical documents. An interface is also developed to interact with the user. User can issue simple keyword like
queries and documents are retrieved using TF-IDF statistics and BM25 weighting scheme. Retrieved results are ranked in
descending order from the highest relevance score to lowest relevance score. New information can be classified and added
to the index using search interface. The system is designed and developed using the Spiral Model and implemented in
dot.net tools. The survey and interviewing techniques are also used to identify the needs and prioritizing tasks. |
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