A Research For Semantic Relation Automatic Extraction Among Named Entities In Chinese Professional Domain

We are in an era of information explosion, and the Chinese information in rapid growth on the Internet. It is crucial to automatically collect the needful information for users by information extraction technology from the large-scale Chinese information. And the semantic relation extraction among named entities is one of major tasks in information extraction. Therefore, in recent years, the research of Chinese semantic relation extraction among named entities has become a hot field in natural language processing research in our country.A majority of current methods of Chinese relation extraction are supervised or weakly supervised. And their research objects are corpuses in common domain. There ways are time-consuming and laborious in tagging training corpuses, making relation extraction rules and selecting initial relation seeds. In addition, those methods sometimes are not applicable in certain professional corpuses. Therefore, this paper presents an unsupervised method to discover the semantic relations among named entities in professional corpuses. And this paper achieves the system. In addition, we attempt to use the extracted results of this system to construct the relation templates and relation seeds.According to the characteristics of corpus in professional field, we optimized vector space model adopting some linguistic tool to overcome the blurry feature of professional corpus. Then we proposed a method to construct entity-relation network according to the feature of latent relation information distribution. And then, we extracted relations automatically utilizing community characteristic in complex networks. Finally, By importance analysis of words in context, we use the words with highest weight as key words to describe relations.We tested our system in the corpus of professional field and evaluated it using standard method. We also constructed a supervised relation extraction system to verify the result of the system. Result indicated that the system can get description among named entities rapidly and accurately while unsupervised. And it could get almost all the known relations, even some kind of unknown relations.Experiment shows good performance of our system in both professional field and unsupervised procedure. It also proves that the result of unsupervised relation extraction could assist supervised method. In addition, the relation descriptions of our result can provide basis for the construction of ontology in professional field.