A crime reports analysis system to identify related crimes

Journal of the American Society for Information Science and Technology

Volumn 62, Issue 8, 2011, Pages 1533-1547

  Ku, Chih Hao ^a   Leroy, Gondy ^a  

^a CLAREMONT GRADUATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS SYSTEM; COSINE SIMILARITY; CRIME INFORMATIONS; DICE COEFFICIENT; DOCUMENT SIMILARITY; DOMAIN SPECIFIC; ESSENTIAL COMPONENT; GENERIC ALGORITHM; GOLD STANDARDS; JACCARD COEFFICIENTS; LAW ENFORCEMENT PERSONNEL; SIMILARITY ALGORITHM; SIMILARITY SCORES; TEXT MESSAGING; TEXTUAL INFORMATION; TIME-CONSUMING TASKS; VIDEO CLIPS;

ALGORITHMS; COMPUTER PROGRAMMING; CRIME; EQUIPMENT TESTING; RATING;

COMPUTER CRIME;

EID: 79960295603     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.21552     Document Type: Article

References (59)

1. Aliguliyev, R.M., (2009). A new sentence similarity measure and sentence based extractive technique for automatic text summarization. Expert Systems with Applications, 36 (4), 7764-7772.
2. Ananthanarayanan, R., Chenthamarakshan, V., Deshpande, P.M., &, Krishnapuram, R., (2008). Rule based synonyms for entity extraction from noisy text. In Proceedings of the Second Workshop on Analytics for Noisy Unstructured Text Data (AND '08) (pp. 31-38). New York: ACM Press.
3. Bhagwat, D., Eshghi, K., &, Mehra, P., (2007). Content-based document routing and index partitioning for scalable similarity-based searches in a large corpus. Proceedings of the 13th ACM Special Interest Group on Knowledge Discovery and Data Mining International Conference on Knowledge Discovery and Data Mining (KDD '07) (pp. 105-112). San Jose, CA: ACM.
4. Bilenko, M., &, Mooney, R.J., (2003). Adaptive duplicate detection using learnable string similarity measures. In Proceedings of the Ninth ACM ACM Special Interest Group on Knowledge Discovery and Data Mining International Conference on Knowledge Discovery and Data Mining (KDD-2003) (pp. 39-48). New York: ACM Press.
5. Boutari, A.M., Carpineto, C., &, Nicolussi, R., (2010). Evaluating term concept association measures for short text expansion: Two case studies of classification and clustering. In Proceedings of the Seventh International Conference on Concept Lattices and Their Applications (CLA 2010) (pp. 1-12). Seville. Spain: University of Sevilla.
6. Budanitsky, A., &, Hirst, G., (2006). Evaluating WordNet-based measures of lexical semantic relatedness. Computational Linguistics, 32 (1), 13-47. (Pubitemid 43764024)
7. Bundschus, M., Dejori, M., Stetter, M., Tresp, V., &, Kriegel, H.-P., (2008). Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics, 9, 1-14.
8. Bunescu, R., Ge, R., Kate, R.J., Marcotte, E.M., Mooney, R.J., Ramani, A.K., &, Wong, Y.W., (2005). Comparative experiments on learning information extractors for proteins and their interactions. Artificial Intelligence in Medicine, 33 (2), 139-155. (Pubitemid 40462453)
9. Califf, M.E., &, Mooney, R.J., (2003). Bottom-up relational learning of pattern matching rules for information extraction. Journal of Machine Learning Research, 4, 177-210.
10. Carullo, M., Binaghi, E., &, Gallo, I., (2009). An online document clustering technique for short Web contents. Pattern Recognition Letters, 30 (10), 870-876.
11. Chau, M., Xu, J.J., &, Chen, H., (2002). Extracting meaningful entities from police narrative reports. ACM International Conference Proceeding Series (Vol. 129, pp. 1-5). Los Angeles, CA: Digital Government Research Center.
12. Chen, H., Chung, W., Xu, J.J., Wang, G., Qin, Y., &, Chau, M., (2004). Crime data mining: A general framework and some examples. Computer, 37 (4), 50-56.
13. Cowie, J., &, Lehnert, W., (1996). Information extraction. Communications of the ACM, 39 (1), 80-91. (Pubitemid 126442984)
14. Cunningham, H., (2002). GATE-A general architecture for text engineering. Computers and the Humanities, 36 (2), 223-254. (Pubitemid 44947904)
15. Day, M.-Y., Tsai, R.T.-H., Sung, C.-L., Hsieh, C.-C., Lee, C.-W., Wu, S.-H., Wu, K.-P., Ong, C.-S., ..., Hsu, W.-L., (2007). Reference metadata extraction using a hierarchical knowledge representation framework. Decision Support Systems, 43 (1), 152-167. (Pubitemid 46073233)
16. Ernesto William De, L., &, Andreas, N., (2006). Rebuilding lexical resources for information retrieval using sense folder detection and merging methods. In Proceedings of the fifth International Conference on Language Resources and Evaluation (LREC '06) (pp. 99-102). Genova, Italy: European Language Resources Association.
17. FBI. (2009). To track a threat-Inside our internet tip line. Retrieved from
18. Feldman, R., Rosenfeld, B., &, Fresko, M., (2006). TEG-A hybrid approach to information extraction. Knowledge and Information Systems, 9 (1), 1-18. (Pubitemid 43231144)
19. Fluss, R., Faraggi, D., &, Reiser, B., (2005). Estimation of the Youden Index and its associated cutoff point. Biometrical Journal, 47 (4), 458-472. (Pubitemid 41229379)
20. Gregg, D.G., &, Walczak, S., (2007). Exploiting the Information Web. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37 (1), 109-125. (Pubitemid 46002494)
21. Grishman, R., &, Sundheim, B., (1996). Message Understanding Conference-6: A brief history. Proceedings of the 16th Conference on Computational Linguistics (Vol. 1, pp. 466-471). Copenhagen, Denmark: Association for Computational Linguistics.
22. Iriberri, A., Leroy, G., &, Garrett, N., (2006). Reporting on-campus crime online: User intention to use. In Proceedings of the 39th Hawaii International Conference on System Sciences (Vol. 4, p. 82a). Washington, DC: IEEE Computer Society. (Pubitemid 44539359)
23. Islam, A., &, Inkpen, D., (2008). Semantic text similarity using corpus-based word similarity and string similarity. ACM Transactions on Knowledge Discovery From Data, 2 (2), 1-25.
24. Islam, A., &, Inkpen, D., (2009). Real-word spelling correction using Google Web IT 3-grams. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (EMNLP '09) (pp. 1241-1249). Singapore: Association for Computational Linguistics.
25. Jiang, J.J., &, Conrath, D.W., (1997). Semantic similarity based on corpus statistics and lexical taxonomy. In Proceedings of the International Conference Research on Computational Linguistics (ROCLING X) (pp. 19-33), Taipei, Taiwan.
26. Kaiser, F., Schwarz, H., &, Jakob, M., (2009). Using Wikipedia-based conceptual contexts to calculate document similarity. In Proceedings of the Third International Conference on Digital Society (ICDS '09) (pp. 322-327). Washington, DC: IEEE Computer Society.
27. Kanable, R., (2008). Talking to tipsters. Law Enforcement Technology, 35 (11), 10-18.
28. Khandelwal, V., Gupta, R., &, Allan, J., (2001). An evaluation corpus for temporal summarization. Proceedings of the First International Conference on Human Language Technology Research (HLT '01) (pp. 1-5). San Diego, CA: Association for Computational Linguistics.
29. Ko, Y., Park, J., &, Seo, J., (2004). Improving text categorization using the importance of sentences. Information Processing & Management, 40 (1), 65-79.
30. Konchady, M., (2006). Information extraction. In Text Mining Application Programming (pp. 151-182). Boston, MA: Charles River Media, Inc.
31. Krishna, S., Pingali, P., &, Varma, V., (2010). An information retrieval approach to spelling suggestion. In Proceedings of the 19th International Conference on World Wide Web (WWW '10) (pp. 1135-1136). New York: ACM Press.
32. Ku, C.H., Iriberri, A., &, Leroy, G., (2008a). Crime information extraction from police and witness narrative reports. In Proceedings of the IEEE International Conference on Technologies for Homeland Security (pp. 193-198). Washington, DC: IEEE Computer Society.
33. Ku, C.H., Iriberri, A., &, Leroy, G., (2008b). Natural language processing and e-government: Crime information extraction from heterogeneous data sources. In Proceedings of the 2008 International Conference on Digital Government Research (Vol. 289, pp. 162-170). Montreal, Canada: Digital Government Research Center.
34. Li, Y., Bandar, Z.A., &, McLean, D., (2003). An approach for measuring semantic similarity between words using multiple information sources. IEEE Transactions on Knowledge and Data Engineering, 15 (4), 871-882.
35. Li, Y., McLean, D., Bandar, Z.A., O'Shea, J.D., &, Crockett, K., (2006). Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering, 18 (8), 1138-1150. (Pubitemid 44109541)
36. Malakasiotis, P., &, Androutsopoulos, I., (2007). Learning textual entailment using SVMs and string similarity measures. In Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing (RTE '07) (pp. 42-47). Morristown, NJ: Association for Computational Linguistics.
37. Masrah Azrifah Azmi, M., &, Trevor, M., (2007). Similarity-based estimation for document summarization using fuzzy sets. International Journal of Computer Science and Security, 1 (4), 1-12.
38. Matuszek, C., Cabral, J., Witbrock, M., &, DeOliveira, J., (2006). An introduction to the syntax and content of Cyc. In Proceedings of the Association for the Advancement of Artificial Intelligence Spring Symposium on Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering (pp. 44-49). Menlo Park, CA: Association for the Advancement of Artificial Intelligence. (Pubitemid 44230601)
39. Memon, N., Qureshi, A., Hicks, D., &, Harkiolakis, N., (2008). Extracting information from semi-structured Web documents: A framework. In, Y. Ishikawa, J. He, G. Xu, Y. Shi, G. Huang, C. Pang, Q. Zhang, &, G. Wang, (Eds.), Advanced Web and network technologies, and applications (Vol. 4977, pp. 54-64). Berlin, Germany: Springer.
40. Meng, X., Wang, H., Hu, D., &, Li, C., (2003). A supervised visual wrapper generator for Web-data extraction. In Proceedings of the ACM Special Interest Group on Management of Data (pp. 657-657). New York: ACM Press.
41. Michelakis, E., Krishnamurthy, R., Haas, P.J., &, Vaithyanathan, S., (2009). Uncertainty management in rule-based information extraction systems. In Proceedings of the 35th Special Interest Group on Management of Data International Conference (SIGMOD '09) (pp. 101-114). New York: ACM Press.
42. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., &, Miller, K.J., (1990). Introduction to WordNet: An on-line lexical database. International Journal of Lexicograph, 3 (4), 235-244.
43. Miller, G.A., &, Charles, W.G., (1991). Contextual correlates of semantic similarity. Language and Cognitive Processes, 6 (1), 1-28.
44. Ono, T., Hishigaki, H., Tanigami, A., &, Takagi, T., (2001). Automated extraction of information on protein-protein interactions from the biological literature. Bioinformatics, 17 (2), 155-161. (Pubitemid 32208524)
45. Pedersen, T., Pakhomov, S.V.S., Patwardhan, S., &, Chute, C.G., (2007). Measures of semantic similarity and relatedness in the biomedical domain. Journal of Biomedical Informatics, 40 (3), 288-299. (Pubitemid 46720256)
46. Peng, F., &, McCallum, A., (2006). Information extraction from research papers using conditional random fields. Information Processing & Management, 42 (4), 963-979. (Pubitemid 41828296)
47. Pyysalo, S., Ginter, F., Heimonen, J., Björne, J., Boberg, J., &, Järvinen, J., et al. (2007). BioInfer: A corpus for information extraction in the biomedical domain. BMC Bioinformatics, 8 (1), 50.
48. Resnik, P., (1995). Disambiguating noun groupings with respect to WordNet senses. In Proceedings of the Third Workshop on Very Large Corpora (pp. 54-68). Morristown, NJ: Association for Computational Linguistics.
49. Resnik, P., (1999). Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 11, 95-130. (Pubitemid 129628761)
50. Sánchez-de-Madariaga, R., &, Fernández-del-Castillo, J.R., (2009). The bootstrapping of the Yarowsky algorithm in real corpora. Information Processing & Management, 45 (1), 55-69.
51. Tata, S., &, Patel, J.M., (2007). Estimating the selectivity of tf-idf Based cosine similarity predicates. SIGMOD Record, 36, 75-80. (Pubitemid 351500305)
52. Turmo, J., Ageno, A., &, Català, N., (2006). Adaptive information extraction. ACM Computing Surveys, 38 (2), 1-47.
53. Turney, P.D., (2006). Similarity of semantic relations. Computational Linguistics, 32 (3), 379-416. (Pubitemid 44387125)
54. Wang, D., Li, T., Zhu, S., &, Ding, C., (2008). Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization. In Proceedings of the 31st annual International ACM Special Interest Group on Information Retrieval Conference on Research and Development in Information Retrieval (SIGIR '08) (pp. 307-314). New York: ACM Press.
55. Wellner, B., Castaño, J., &, Pustejovsky, J., (2005). Adaptive string similarity metrics for biomedical reference resolution. In Proceedings of BioLink 2005 (pp. 9-16). Detroit, MI: Association for Computational Linguistics.
56. Wong, T.-L., &, Lam, W., (2007). Adapting Web Information extraction knowledge via mining site-invariant and site-dependent features. ACM Transactions on Internet Technology, 7 (1), 1-40.
57. Yarkoni, T.A.L., Balota, D., &, Yap, M., (2008). Moving beyond Coltheart's N: A new measure of orthographic similarity. Psychonomic Bulletin & Review, 15 (5), 971-979.
58. Zelenko, D., Aone, C., &, Richardella, A., (2003). Kernel methods for relation extraction. Journal of Machine Learning Research, 3, 1083-1106.
59. Zhu, Y., Zhang, R., &, Ahmad, I., (2010). Applying concept similarity to the evaluation of common understanding in multidisciplinary learning. Journal of Computing in Civil Engineering, 24 (4), 335-344.