With the growing interest in opinion mining from web data, more works are focused on mining in English and Chinese reviews. Probing into the problem of product opinion mining, this paper describes the details of our language resources, and imports them into the task of extracting product feature and sentiment task. Different from the traditional unsupervised methods, a supervised method is utilized to identify product features, combining the domain knowledge and lexical information. Nearest vicinity match and syntactic tree based methods are proposed to identify the opinions regarding the product features. Multi-level analysis module is proposed to determine the sentiment orientation of the opinions. With the experiments on the electronic reviews of COAE 2008, the validities of the product features identified by CRFs and the two opinion words identified methods are testified and compared. The results show the resource is well utilized in this task and our proposed method is valid.

Corpus-based approaches and statistical approaches have been the main stream of natural language processing research for the past two decades. Language resources play a key role in such approaches, but there is an insufficient amount of language resources in many Asian languages. In this situation, standardisation of language resources would be of great help in developing resources in new languages. This paper presents the latest development efforts of our project which aims at creating a common standard for Asian language resources that is compatible with an international standard. In particular, the paper focuses on i) lexical specification and data categories relevant for building multilingual lexical resources for Asian languages; ii) a core upper-layer ontology needed for ensuring multilingual interoperability and iii) the evaluation platform used to test the entire architectural framework.