Building New Field Association Term Candidates Automatically by Search Engine
With increasing popularity of the Internet and tremendous amount of on-line text, automatic document classification is important for organizing huge amounts of data. Readers can know the subject of many document fields by reading only some specific Field Association (FA) words. Document fields can be decided efficiently if there are many FA words and if the frequency rate is high. This paper proposes a method for automatically building new FA words. A WWW search engine is used to extract FA word candidates from document corpora. New FA word candidates in each field are automatically compared with previously determined FA words. Then new FA words are appended to an FA word dictionary. From the experiential results, our new system can automatically appended around 44% of new FA words to the existence FA word Dictionary. Moreover, the concentration ratio 0.9 is also effective for extracting relevant FA words that needed for the system design to build FA words automatically.
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- 1.Aoe, J., Morita, K., Mochizuki, H.: An Efficient Retrieval Algorithm of Collocate Information Using Tree Structure. Transaction of The IPSJ 39(9), 2563–2571 (1989)Google Scholar
- 2.Atlam, E.-S., Elmarhomy, G., Morita, K., Fuketa, M., Aoe, J.: A New Algorithm for Construction Specific Field Terms Using Co-occurrence Words Information. In: 8th International Conference on Knowledge-Based Intelligent Information & Engineering Systems, Wellington, New Zealand, Part 1, pp. 530–540 (2004)Google Scholar
- 3.Atlam, E.-S., Aoe, J.: A new algorithm for automatic extracting FA word candidates from document corpora. The Interim Report of Tokushima University, 25-27 (2004)Google Scholar
- 6.Callen, J.P.: Passage and level evidence in document retrieval. In: Proc. of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 302–310 (1994)Google Scholar
- 7.Dozawa, T.: Innovative Multi Information Dictionary Imidas 1999. Annual Series. Zueisha Publication Co, Japan (1999) (In Japanese)Google Scholar
- 9.Fukumoto, F., Suzuki, Y.: Automatic Clustering of Articles using Dictionary definitions. In: Proceeding of the 16th International Conference on Computional Linguistic (COLING 1996), pp. 406–411 (1996)Google Scholar
- 10.Iwayama, M., Tokunaga, T.: Probabilistic Passage Categorization and Its Application. Journal of Natural language Processing 6(3), 181–198 (1999)Google Scholar
- 11.Kawabe, K., Matsumoto, Y.: Acquisition of normal lexical knowledge based on basic level category. Information Processing Society of Japan, SIG note NL125-9, 87–92 (1998)Google Scholar
- 13.Ohkubo, M., Sugizaki, M., Inoue, T., Tanaka, K.: Extracting Information Demand by Analyzing a WWW Search Login. Trans. of Information Processing Society of Japan 39(7), 2250–2258 (1998)Google Scholar
- 14.Salton, G., McGill, M.J.: Introduction of Modern Information Retrieval. McGraw-Hill, New York (1983)Google Scholar
- 15.Tsuji, T., Fuketa, M., Morita, K., Aoe, J.: An Efficient Method of Determining FA Terms of Compound Words. Journal of Natural Language Processing 7(2), 3–26 (2000)Google Scholar