Name
Abstract | ||
|---|---|---|
Address standardization is a very challenging task in data cleansing. To provide better customer relationship management and business intelligence for customer-oriented cooperates, millions of free-text addresses need to be converted to a standard format for data integration, de-duplication and householding. Existing commercial tools usually employ lots of hand-craft, domain-specific rules and reference data dictionary of cities, states etc. These rules work better for the region they are designed. However, rule-based methods usually require more human efforts to rewrite these rules for each new domain since address data are very irregular and varied with countries and regions. Supervised learning methods usually are more adaptable than rule-based approaches. However, supervised methods need large-scale labeled training data. It is a labor-intensive and time-consuming task to build a large-scale annotated corpus for each target domain. For minimizing human efforts and the size of labeled training data set, we present a free-text address standardization method with latent semantic association (LaSA). LaSA model is constructed to capture latent semantic association among words from the unlabeled corpus. The original term space of the target domain is projected to a concept space using LaSA model at first, then the address standardization model is active learned from LaSA features and informative samples. The proposed method effectively captures the data distribution of the domain. Experimental results on large-scale English and Chinese corpus show that the proposed method significantly enhances the performance of standardization with less efforts and training data. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1145/1557019.1557144 | KDD |
Keywords | Field | DocType |
target domain,training data,latent semantic association,address data,lasa model,address standardization,data integration,reference data dictionary,data distribution,human effort,customer relationship management,rule based,supervised learning,data integrity,active learning,data cleansing,reference data,business intelligence | Data integration,Reference data (financial markets),Customer relationship management,Data mining,Data cleansing,Semantic association,Computer science,Supervised learning,Artificial intelligence,Business intelligence,Standardization,Machine learning | Conference |
Citations | PageRank | References |
7 | 0.66 | 21 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Honglei Guo | 1 | 265 | 14.36 |
| Huijia Zhu | 2 | 139 | 6.97 |
| Zhili guo | 3 | 264 | 12.46 |
| Xiaoxun Zhang | 4 | 336 | 16.74 |
| Zhong Su | 5 | 2282 | 110.39 |