汉语专业领域命名实体语义关系自动抽取研究-计算机软件与理论专业论文.docxVIP

⑨ ⑨ 硕士学位论文 MASTER’STHESI$ Abstract We ale in all era of information explosion,and the Chines．information in rapid growth on the Internet．It is crucial to automatically collect the needful information for use侣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 l谢tlLral language processing research in OUr country． A majonty of current methods of Chinese relation extraction are super“sed or weakly supervised．And their research objects arc corpusos in comlTlon domain．There ways are time-consuming and laborious in mg#ng training corpusos，making relation extraction rules and selecting initial relation seeds．In addition,those methods sometimes am not applicable in certain professional corpuscs．Therefore．this paper presents art unsupervised method to discover the semantic relations among named entities in professional corpuses．And this paper achieves the system．In addition,we attempt to心 the extracted results ofthis system tO constl-uet the relation templates and relation seeds． According to the characteristics ofcorpus in professional field，we optimized vector space model adopting some linguistic tool to overcome the blurry feature ofprofessional 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 pmfessional field and evaluated it using standard method．We also consWacted a supervised relation extraction system to veilfy the result of the system．Result indicated that the sys