TY - GEN
T1 - UB at GeoCLEF 2006
AU - Ruiz, Miguel E.
AU - Abbas, June
AU - Mark, David
AU - Shapiro, Stuart
AU - Southwick, Silvia B.
PY - 2007
Y1 - 2007
N2 - This paper summarizes the work done at the State University of New York at Buffalo (UB) in the GeoCLEF 2006 track. The approach presented uses pure IR techniques (indexing of single word terms as well as word bigrams, and automatic retrieval feedback) to try to improve retrieval performance of queries with geographical references. The main purpose of this work is to identify the strengths and shortcomings of this approach so that it serves as a basis for future development of a geographical reference extraction system. We submitted four runs to the monolingual English task, two automatic runs and two manual runs, using the title and description fields of the topics. Our official results are above the median system (auto=0.2344 MAP, manual=0.2445 MAP). We also present an unofficial run that uses title description and narrative which shows a 10% improvement in results with respect to our baseline runs. Our manual runs were prepared by creating a Boolean query based on the topic description and manually adding terms from geographical resources available on the web. Although the average performance of the manual run is comparable to the automatic runs, a query by query analysis shows significant differences among individual queries. In general, we got significant improvements (more that 10% average precision) in 8 of the 25 queries. However, we also noticed that 5 queries in the manual runs perform significantly below the automatic runs.
AB - This paper summarizes the work done at the State University of New York at Buffalo (UB) in the GeoCLEF 2006 track. The approach presented uses pure IR techniques (indexing of single word terms as well as word bigrams, and automatic retrieval feedback) to try to improve retrieval performance of queries with geographical references. The main purpose of this work is to identify the strengths and shortcomings of this approach so that it serves as a basis for future development of a geographical reference extraction system. We submitted four runs to the monolingual English task, two automatic runs and two manual runs, using the title and description fields of the topics. Our official results are above the median system (auto=0.2344 MAP, manual=0.2445 MAP). We also present an unofficial run that uses title description and narrative which shows a 10% improvement in results with respect to our baseline runs. Our manual runs were prepared by creating a Boolean query based on the topic description and manually adding terms from geographical resources available on the web. Although the average performance of the manual run is comparable to the automatic runs, a query by query analysis shows significant differences among individual queries. In general, we got significant improvements (more that 10% average precision) in 8 of the 25 queries. However, we also noticed that 5 queries in the manual runs perform significantly below the automatic runs.
UR - https://www.scopus.com/pages/publications/38049171047
U2 - 10.1007/978-3-540-74999-8_126
DO - 10.1007/978-3-540-74999-8_126
M3 - Conference contribution
AN - SCOPUS:38049171047
SN - 9783540749981
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 978
EP - 985
BT - Evaluation of Multilingual and Multi-modal Information Retrieval - 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006, Revised Selected Papers
PB - Springer Verlag
T2 - 7th Workshop of the Cross-Language Evaluation Forum, CLEF 2006
Y2 - 20 September 2006 through 22 September 2006
ER -