Domain adaptation is an important problem in named entity recognition ( NER ) . NER classifiers usually lose accuracy in the domain transfer due to the different data distribution between the source and the target domains . The major reason for performance degrading is that each entity type often has lots of domainspecific term representations in the different domains . The existing approaches usually need an amount of labeled target domain data for tuning the original model . However , it is a labor-intensive and time-consuming task to build annotated training data set for every target domain . We present a domain adaptation method with latent semantic association ( LaSA ) . This method effectively overcomes the data distribution difference without leveraging any labeled target domain data . LaSA model is constructed to capture latent semantic association among words from the unlabeled corpus . It groups words into a set of concepts according to the related context snippets . In the domain transfer , the original term spaces of both domains are projected to a concept space using LaSA model at first , then the original NER model is tuned based on the semantic association features . Experimental results on English and Chinese corpus show that LaSA-based domain adaptation significantly enhances the performance of NER . Named entities ( NE ) are phrases that contain names of persons , organizations , locations , etc . NER is an important task in information extraction and natural language processing ( NLP ) applications . Supervised learning methods can effectively solve NER problem by learning a model from manually labeled data ( Borthwick , 1999 ; Sang and Meulder , 2003 ; Gao et al . , 2005 ; Florian et al . , 2003 ) . However , empirical study shows that NE types have different distribution across domains ( Guo et al . , 2006 ) . Trained NER classifiers in the source domain usually lose accuracy in a new target domain when the data distribution is different between both domains . Domain adaptation is a challenge for NER and other NLP applications . In the domain transfer , the reason for accuracy loss is that each NE type often has various specific term representations and context clues in the different domains . For example , { " economist " , " singer " , " dancer " , " athlete " , " player " , " philosopher " , ... } are used as context clues for NER . However , the distribution of these representations are varied with domains . We expect to do better domain adaptation for NER by exploiting latent semantic association among words from different domains . Some approaches have been proposed to group words into " topics " to capture important relationships between words , such as Latent Semantic Indexing ( LSI ) ( Deerwester et al . , 1990 ) , probabilistic Latent Semantic Indexing ( pLSI ) ( Hofmann , 1999 ) , Latent Dirichlet Allocation ( LDA ) ( Blei et al . , 2003 ) . These models have been successfully employed in topic modeling , dimensionality reduction for text categorization ( Blei et al . , 2003 ) , ad hoc IR ( Wei and Croft . , 2006 ) , and so on . In this paper , we present a domain adaptation method with latent semantic association . We focus on capturing the hidden semantic association among words in the domain adaptation . We introduce the LaSA model to overcome the distribution difference between the source domain and the target domain . LaSA model is constructed from the unlabeled corpus at first . It learns latent semantic association among words from their related context snippets . In the domain transfer , words in the corpus are associated with a low-dimension concept space using LaSA model , then the original NER model is tuned using these generated semantic association features . The intuition behind our method is that words in one concept set will have similar semantic features or latent semantic association , and share syntactic and semantic context in the corpus . They can be considered as behaving in the same way for discriminative learning in the source and target domains . The proposed method associates words from different domains on a semantic level rather than by lexical occurrence . It can better bridge the domain distribution gap without any labeled target domain samples . Experimental results on English and Chinese corpus show that LaSA-based adaptation significantly enhances NER performance across domains . The rest of this paper is organized as follows . Section 2 briefly describes the related works . Section 3 presents a domain adaptation method based on latent semantic association . Section 4 illustrates how to learn LaSA model from the unlabeled corpus . Section 5 shows experimental results on large-scale English and Chinese corpus across domains , respectively . The conclusion is given in Section 6 . We present a domain adaptation method with LaSA model in this paper . LaSA model captures latent semantic association among words from the unlabeled corpus . It better groups words into a set of concepts according to the related context snippets . LaSAbased domain adaptation method projects words to a low-dimension concept feature space in the transfer . It effectively overcomes the data distribution gap across domains without using any labeled target domain data . Experimental results on English and Chinese corpus show that LaSA-based domain adaptation significantly enhances the performance of NER across domains . Especially , LaSA model effectively increases the accuracy of each NE type recognition in the domain transfer . Moreover , LaSA-based domain adaptation method works well across languages . To further reduce the accuracy loss , we will explore informative sampling to capture fine-grained data difference in the domain transfer .

Challenge: Named entity recognition classifiers lose accuracy in domain transfers because each entity type has domain-specific term representations and existing approaches require expensive labeled data.
Approach: They propose to capture latent semantic associations among words in the unlabeled corpus and use them to tune original named entity models.
Outcome: The proposed model improves the performance on the English and Chinese corpus across domains especially on each NE type recognition.