Word association norms, mutual information, and lexicography
Computational Linguistics
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Automatic segmentation of text into structured records
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Active Learning for Natural Language Parsing and Information Extraction
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
The Journal of Machine Learning Research
Mining reference tables for automatic text segmentation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning for statistical natural language parsing
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Named entity recognition through classifier combination
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A robust risk minimization based named entity recognition system
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
HowtogetaChineseName(Entity): segmentation and combination issues
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Multi-criteria-based active learning for named entity recognition
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Empirical study on the performance stability of named entity recognition model across domains
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Feature-based opinion mining and ranking
Journal of Computer and System Sciences
Towards the automation of address identification
Scientometrics
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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.