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 .
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.
They propose to capture latent semantic associations among words in the unlabeled corpus and use them to tune original named entity models.
The proposed model improves the performance on the English and Chinese corpus across domains especially on each NE type recognition.