Study on Product Named Entity Recognition for Business Information Extraction
Liu Fei-fan, R Fujitsu · Zhongwen xinxi xuebao · 2006
Electronic business has fueled increasing research interest recently in business information extraction and market intelligence management.As one of the key techniques,product named entity recognition(product NER) has also begun to draw more attention in the field of natural language processing.In the paper,characteristics and challenges in product NER are explored and analyzed deliberately,and a hierarchical hidden Markov model(HHMM) based approach to product NER from Chinese free text is presented.Experimental results in both digital and mobile phone domains show that our approach performs quite well in these two different domains and achieves F-measures of 79.7%,86.9%,75.8% on the whole for three types of product named entities respectively.In comparison with maximum entropy model,HHMM is experimentally proved to be more powerful for dealing with multi-scale embedded sequence problem.