PENINGKATAN KINERJA EKSPANSI QUERY DENGAN PSEUDO RELEVANCE FEEDBACK DAN SIMILARITAS WU-PALMER PADA SISTEM TEMU BALIK LINTAS BAHASA

• Main Author: Akmal Pratama, Muhammad
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/66516
• Institution: Institut Teknologi Bandung

PENINGKATAN KINERJA EKSPANSI QUERY DENGAN PSEUDO RELEVANCE FEEDBACK DAN SIMILARITAS WU-PALMER PADA SISTEM TEMU BALIK LINTAS BAHASA Oleh Muhammad Akmal Pratama NIM : 13515135 With more information available in multiple languages, the need to search for relevant information no longer fixated only on one language. Cross language information retrieval, a system that search for relevant information in different language, experienced a decrease in performance due to the loss of some meanings during translation process or the initial query that was not descriptive of the information to be sought. One method to improve the performance of the information retrieval system relevance feedback query expansion. Another method utilizes external resources such as WordNet as the basis for generating query expansion term. This study utilizes the Wu-Palmer semantic similarity measurement in WordNet to improve the performance of pseudo relevance feedback query expansion. The terms contained in the feedback document are considered as candidate expansion terms. Each candidate is given weight based on IDF score, Wu-Palmer similarity score, and document score using Okapi ranking function. A few N candidate terms with the heighest weight are used in query expansion. The results on CRAN collection show that the query expansion method of pseudo relevance feedback with Wu-Palmer semantic similarity can have better performance than the Rocchio pseudo relevance feedback for document feedback less than five. The highest mean average precision is obtained when one document feedback and two expansion term is used with 0.6225 compared to Rocchio pseudo relevance feedback with only 0.5502. Keywords—information retrieval, query expansion, Wu-Palmer similarity, relevance feedback, mean average precision