Progress in Open Information Extraction
Bo Yang · Zhongwen xinxi xuebao · 2014
Extracting useful information automatically from large-scale unstructured texts has been a long-standing goal of NLP and AI.And open information extraction is now widely pursued for effective web information acquisition.Open information extraction can be divided into dual and n-tuple entity relation extraction according to the number of arguments involved.In accordance with these two aspects,this paper analyses several typical methods for open relation extraction together with their defects.It is indicated that most current methods still belong to shallow semantic processing,hardly considering the implicit relation.Therefore,it is beleved that the adoption of joint inference strategy such as the markov logic and the ontology structure based inference can take advantage of multiple features.The combination of open and open up apromising prospect to infer the fine and full information for open information extraction.