Standard
Improving full text search with text mining tools. /
Piao, Scott; Rea, Brian; McNaught, John et al.
Natural language processing and information systems: 14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers. ed. / Helmut Horacek; Elisabeth Métais; Rafael Muñoz; Magdalena Wolska. Berlin: Springer, 2010. p. 301-302 (Lecture Notes in Computer Science; Vol. 5723).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Harvard
Piao, S, Rea, B, McNaught, J & Ananiadou, S 2010,
Improving full text search with text mining tools. in H Horacek, E Métais, R Muñoz & M Wolska (eds),
Natural language processing and information systems: 14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers. Lecture Notes in Computer Science, vol. 5723, Springer, Berlin, pp. 301-302.
https://doi.org/10.1007/978-3-642-12550-8_29
APA
Piao, S., Rea, B., McNaught, J., & Ananiadou, S. (2010).
Improving full text search with text mining tools. In H. Horacek, E. Métais, R. Muñoz, & M. Wolska (Eds.),
Natural language processing and information systems: 14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers (pp. 301-302). (Lecture Notes in Computer Science; Vol. 5723). Springer.
https://doi.org/10.1007/978-3-642-12550-8_29
Vancouver
Piao S, Rea B, McNaught J, Ananiadou S.
Improving full text search with text mining tools. In Horacek H, Métais E, Muñoz R, Wolska M, editors, Natural language processing and information systems: 14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers. Berlin: Springer. 2010. p. 301-302. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-12550-8_29
Author
Piao, Scott ; Rea, Brian ; McNaught, John et al. /
Improving full text search with text mining tools. Natural language processing and information systems: 14th International Conference on Applications of Natural Language to Information Systems, NLDB 2009, Saarbrücken, Germany, June 24-26, 2009. Revised Papers. editor / Helmut Horacek ; Elisabeth Métais ; Rafael Muñoz ; Magdalena Wolska. Berlin : Springer, 2010. pp. 301-302 (Lecture Notes in Computer Science).
Bibtex
@inbook{482dc2bfb9814aa6a2f640df8e25535d,
title = "Improving full text search with text mining tools",
abstract = "Today, academic researchers face a flood of information. Full text search provides an important way of finding useful information from mountains of publications, but it generally suffers from low precision, or low quality of document retrieval. A full text search algorithm typically examines every word in a given text, trying to find the query words. Unfortunately, many words in natural language are polysemous, and thus many documents retrieved using this approach are irrelevant to actual search queries.",
keywords = "Information Retrieval, Full Text Search, Term extraction, Termine, Document clustering, Natural Language processing",
author = "Scott Piao and Brian Rea and John McNaught and Sophia Ananiadou",
year = "2010",
doi = "10.1007/978-3-642-12550-8_29",
language = "English",
isbn = "9783642125492",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "301--302",
editor = "Helmut Horacek and Elisabeth M{\'e}tais and Rafael Mu{\~n}oz and Magdalena Wolska",
booktitle = "Natural language processing and information systems",
}
RIS
TY - CHAP
T1 - Improving full text search with text mining tools
AU - Piao, Scott
AU - Rea, Brian
AU - McNaught, John
AU - Ananiadou, Sophia
PY - 2010
Y1 - 2010
N2 - Today, academic researchers face a flood of information. Full text search provides an important way of finding useful information from mountains of publications, but it generally suffers from low precision, or low quality of document retrieval. A full text search algorithm typically examines every word in a given text, trying to find the query words. Unfortunately, many words in natural language are polysemous, and thus many documents retrieved using this approach are irrelevant to actual search queries.
AB - Today, academic researchers face a flood of information. Full text search provides an important way of finding useful information from mountains of publications, but it generally suffers from low precision, or low quality of document retrieval. A full text search algorithm typically examines every word in a given text, trying to find the query words. Unfortunately, many words in natural language are polysemous, and thus many documents retrieved using this approach are irrelevant to actual search queries.
KW - Information Retrieval
KW - Full Text Search
KW - Term extraction
KW - Termine
KW - Document clustering
KW - Natural Language processing
U2 - 10.1007/978-3-642-12550-8_29
DO - 10.1007/978-3-642-12550-8_29
M3 - Chapter
SN - 9783642125492
T3 - Lecture Notes in Computer Science
SP - 301
EP - 302
BT - Natural language processing and information systems
A2 - Horacek, Helmut
A2 - Métais, Elisabeth
A2 - Muñoz, Rafael
A2 - Wolska, Magdalena
PB - Springer
CY - Berlin
ER -
