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1 Vol. 42 No. 6 June 2001 IREX-NE F A Japanese Named Entity Extraction System Based on Building a Large-scale and High-quality Dictionary and Pattern-matching Rules Yoshikazu Takemoto, Toshikazu Fukushima and Hiroshi Yamada We have developed a Named Entity extraction system from Japanese text. Named Entities, i.e., proper names and temporal/numerical expressions are considered as the essential elements for extracting information. The system employs a conventional method that it divides input Japanese text into words and parts of speech by morphological analysis and extracts each Named Entity by referencing dictionaries and applying pattern-matching rules. In order to improve the system s accuracy, we aim to build a large-scale and high-quality dictionary and rules. Both the dictionary and rules have been produced manually, because we believe that a hand-made dictionary or rules have better quality than those that are made automatically. We also focused our attention on two points for cases that cannot be covered by the dictionary. One is to extract proper names from compound words, and the other is to designate unknown or vague words as proper names. For the first point, our system divides compound words and determines proper names within them. Thus, omissions of proper names in compound words can be eliminated. For the second point, our system recognizes abbreviations of proper names, which tend to be unknown or vague, using reliable proper names. For the IREX-NE corpus, our system has accomplished as F-measure score. 1. Information Services Department, Information Services Division, NEC Patent Service, Ltd. NEC Internet Systems Research Laboratories, Computer & Communication Media Research, NEC Corporation NEC Open Systems Development Department, 2nd Systems Operations Unit, NEC Corporation 1) 5) 1580

2 Vol. 42 No MUC-6 6) 90% 2) MUC-6 MET-1 7),8) 7 90% 1 MUC IREX-NE 9) 10),11) 1) MUCMET 12) IREX-NE 13) ARPA 1987 MUC 1992 TREC IREX-NE 6 2. MUCMET IREX-NE IREX-NE 14) IREX-NE 8 IREX-NE 15) IREX-NE 8 SGML 1 15) 1 Table 1 Examples of named entity. ORGANIZATION NEC PERSON LOCATION ARTIFACT DATE TIME 5 15 MONEY PERCENT 120%5 1

3 1582 June ) / / /

4 Vol. 42 No Table 2 Examples of rule. 2 1 / / 117 / 22 / // 2 /// ///// //4//1// / 3 2 / / / / /4//1//1 IREX- NE /// // 10 F F 17) MET ) F 5 IREX

5 1584 June (a) (b) (c) (a) (a) (b) (c) (b) (c) (a) 2 / / 8) 18) Fig. 1 Our named entity extraction system configuration.

6 Vol. 42 No Table 3 3 Details of named entity dictionary NEC Lavie / / // / (1) (2) (3) 3 (3) (1) NEC (1) (2) (2) 3 3 /

7 1586 June 2001 / ) 1 3 / / / (A) (B) (C) (A) / (B) / / (C) //// 8)11)12)18)20) 21) Fig. 2 2 An example of our named entity extraction process.

8 Vol. 42 No ) 5. IREX-NE IREX-NE F F R P b (1) IREX-NE Table 4 4 Accuracy of our named entity extraction system. GLD SYS COR R P F F = (1+b2 )PR b 2 P + R (1) GLD SYS COR R GLD COR P SYS COR F R P (1) b=1 (2) F = 2PR P + R (2) 4 F IREX-NE 4

9 1588 June 2001 Table 5 5 Evaluation of each component of our system. A A1 A2 A3 A4 B B1 B2 B3 B4 B5 B F A1A4 A1 A2A1 A3A2 A4 A1 A2 A3 A4 A3 5 A 5 A 38.7% 31.7% 33.9%A2 A3 F A F 17.9% 19.6%F 18.9 A1 A2 5.3 B1 B6 B1 B2B1 B3B2 =A3 B4B3 B5B4=A4 B6B5 B1 B2 B3 B3 5.2 A3 B4 B5 B5 5.2 A4 B B 5B F 3.9%F 2.1 B3 B4 0.9% B2 B3 F 0.5 B4 B5 0.4% 1.2% F 0.2 B1 B2 /

10 Vol. 42 No F Table 6 Accuracy improvement on training corpus. A A1 A2 A3 A4 B B1 B2 B3 B4 B5 B6 A B C ABC F 6 A IREX-NE 46 B IREX-NE 36 C IREX-NE CRL AB C AB A B A 5.4 (1) (2) (1) (1) (2) IREX-NE F %

11 1590 June IREX-NE IREX-NE F NEC MET-1 NEC 1) Vol.40, No.4 (1999). 2) (1996). 3) Vol.36, No.8 (1995). 4) (1996). 5) Cowie, J. and Lehnert, W.: Information Extraction, Comm. ACM, Vol.39, No.1 (1996). 6) Proc. 6th Message Understanding Conference (MUC-6 ), Morgan Kaufman Publishers Inc. (1996). 7) Proc. Tipster Text Program (Phase II ), DARPA (1996). 8) (1996). 9) IREX (1998). 10) Vol.25, No.6 (1984). 11) 35 6S-3 (1987). 12) Takemoto, Y., Wakao, T., Yamada, H., Gaizauskas, R. and Wilks, Y.: Description of NEC/Sheffield System Used For MET Japanese, Proc. Tipster Text Program (Phase II ) (1996). 13) IREX-NE IREX (1999). 14) IREX NE 5 B2-1 (1999). 15) 16) 5 A2-2 (1999). 17) Sekine, S.: NYU: Description of the Japanese NE System Used For MET-2 (1998). 18) (1992). 19) 3 (1997). 20) 53 7L-3 (1996). 21) (1998). ( ) ( )

12 Vol. 42 No NEC NEC 1982 NEC WWW ACM

1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2

1 Fig. 1 Extraction of motion,.,,, 4,,, 3., 1, 2. 2.,. CHLAC,. 2.1,. (256 ).,., CHLAC. CHLAC, HLAC. 2.3 (HLAC ) r,.,. HLAC. N. 2 HLAC Fig. 2 CHLAC 1 2 3 3,. (CHLAC), 1).,.,, CHLAC,.,. Suspicious Behavior Detection based on CHLAC Method Hideaki Imanishi, 1 Toyohiro Hayashi, 2 Shuichi Enokida 3 and Toshiaki Ejima 3 We have proposed a method for