地址資訊和人們的日常生活息息相關，人們常需要透過網路查詢相關實體商店、學校或組織的地址，再經由地圖標示服務確定其實際方位。然而並不是每一個網站同時提供地址與地圖標示的功能，因此本研究目的是希望設計一個能從網頁中自動擷取英文地址的服務，並結合地圖標示功能，將擷取到的地址以及其相關資訊，一併標示在地圖上，提供使用者簡單方便的地圖標記資訊服務。我們的系統分為兩個部分，第一部分，將網頁透過條件式隨機域的方式訓練出地址擷取的模型，輸入的網頁經過此模型的測試過程後並擷取地址；第二部份，則以擷取到的地址為基礎，在網頁中擷取與地址相關的資訊，找出包含地址和相關資訊的地址區塊邊界，並且針對包含多餘資訊的區塊提出調整的作法。實驗結果得知，我們的地址擷取效能可以提升F-measure至0.913，同時對於八成六的資料可以正確的擷取到相關資訊。

Address Information is closely linked to people''s daily life. People often need to query addresses of related brick-and-mortar shopping malls、schools and organization. And using the service of map marking identified the real direction. But there are not all web pages providing addresses and facility of map marking. Therefore, designing a service of extracting English addresses automatically from web pages is the goal of our research. And the service combines the facility of map marking and marks the extracted addresses and the related information on the map. The service provides users in a convenient and easy way to using the information service of map marking. Our system is divided into two steps: the first step is using Conditional Random fields to train the model of address extraction. Page we input enters the testing process of model of address extraction and outputs the segment of address. The second step is using extracted addresses as landmarks to extract related information and finding out the correct boundary of address blocks. In terms of the result of experiment, the F-measure of extraction by Conditional Random field is up to 0.913. And we also propose the method of adjustment to revise the incorrect boundary. The accuracy after adjusting is from 0.8506 to 0.8689.

1. Saeid Asadi, Guowei Yang, Xiaofang Zhou, Yuan Shi, Boxuan Zhai, Wendy Wen-Rong Jiang: Pattern-Based Extraction of Addresses from Web Page Content. APWeb 2008: 407-418.
2. Karla A. V. Borges, Alberto H. F. Laender, Claudia Bauzer Medeiros, Clodoveu A. Davis: Discovering geographic locations in web pages using urban addresses. GIR 2007: 31-36.
3. Karla A. V. Borges. Use of an Ontology of Urban Places for Recognition and Extraction of Geospatial Evidences on the Web ( in Portuguese ). PhD Thesis, Federal University of Minas Gerais : Belo Horizonte ( MG ), Brazil, 2006.
4. Lin Can, Zhang Qian, Xiaofeng Meng, Wenyin Lin: Postal Address Detection from Web Documents. WIRI 2005: 40-45.
5. P. Nagabhushan, S. A. Angadi, Basavaraj S. Anami: A Fuzzy Symbolic Inference System for Postal Address Component Extraction and Labelling. FSKD 2006: 937-946.
6. Wentao Cai, Shengrui Wang, Qingshan Jiang: Address Extraction: Extraction of Location-Based Information from the Web. APWeb 2005: 925-937.
7. Dayne Freitag: Information Extraction from HTML: Application of a General Machine Learning Approach. AAAI/IAAI 1998: 517-523.
8. Thomas G. Dietterich: Machine Learning for Sequential Data: A Review. SSPR/SPR 2002: 15-30.
9. Olga Ourioupina. 2002. Extracting geographical knowledge from the internet. In Proceedings of the ICDMAM International Workshop on Active Mining.
10. W. Daelemans, J. Zavrel, K. van der Sloot, and A. van den Bosch. TiMBL: Tilburg Memory-Based Learner. ILK Technical Report ─ ILK 02-01, Tilburg, 2002.
11. J. R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc, San Francisco, 1993.
12. Uryupina, O. (2003) Semi-supervised learning of geographical gazetteers from the internet. In: Kornai, A. and Sundheim, B. (eds.) Proceedings of the HLT-NAACL 2003 Workshop on Analysis of Geographic References, Alberta,Canada: ACL, 18-25.
13. Zheyuan Yu. High Accuracy Postal Address Extraction From Web Pages. Master Thesis, Dalhousie University . 2007.
14. A. Alberto H. F. Laender, Berthier Ribeiro-Neto, and Altigran S. da Silva. DEByE - Data Extraction by Example. Data and Knowledge Engineering, 2002.
15. Wei Liu, Xiaofeng Meng, Weiyi Meng. ViDE: A Vision-based Approach for Deep Web Data Extraction. Transactions on Knowledge and Data Engineering, IEEE, 2007
16. J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proc. 18th International Conf.on Machine Learning, 2001.
17. Roman Klinger and Katrin Tomanek. Classical Probabilistic Models and Conditional Random Fields. Algorithm Engineering Report TR07-2-013, Department of Computer Science, Dortmund University of Technology, December 2007. ISSN 1864-4503.
18. Charles Sutton and Andrew McCallum. An Introduction to Conditional Random Fields for Relational Learning.In " Introduction to Statistical Relational Learning ." Edited by Lise Getoor and Ben Taskar. MIT Press, 2006.
19. Y. Liu, E. Shriberg, A. Stolcke, and M. Harper. Comparing HMM, Maximum Entropy and Conditional Random Fields for Disfluency Detection. Proceeding of Eurospeech, 2005.
20. CRF++: Yet Another CRFtoolkit：http://crfpp.sourceforge.net/
21. Google MAP API：http://code.google.com/apis/maps/
22. Hanna M. Wallach. Conditiondal Randiom Fields: An Introduction. Technical Report MS-CIS-04-21. Department of Computer and Information Science, University of Pennsylvania, 2004.
23. Alberto H. F. Laender, Berthier A. Ribeiro-Neto. A Brief Survey of Web Data Extraction Tools. SIGMOD Record, Vol. 31, No. 2, June 2002.
24. B. E. Boser, I. M. Guyon, and V. N. Vapnik. “A training algorithm for optimal margin classifier,” In Proc. 5th ACM Workshop on Computational Learning Theory, pp. 144-152, Pittsburgh, PA, July 1992.