Research On Event Extraction For Product Requirement Analysis

With the development of society and technology, our life is full of electronic products, the modern man's office is paperless. But in the face of a large amount of information how to get the key content of the information is the research content of this topic. Event extraction is a key task in information extraction. It is intended to convert unstructured text into structured text and present it. To extract the content that we specify, such as characters, time, place, etc.When using machine learning method to study event extraction, it is needed to have a large scale corpus, which is annotated by hand. But the available corpus is limited. When using statistical model to classify, how to select the appropriate features to accurately describe the data is the core problem of the classification task. In view of these two questions, this paper has done the relevant exploration work.Firstly, this paper develops a text annotation tool, which is used to construct the event corpus for product requirement analysis. When constructing a corpus, this paper first analyzes the Chinese sentence structure and syntax, which lays the foundation for the later event classification and event marking.Secondly, in order to study the event extraction task, this paper respectively to the event type recognition and event element extraction research status are summarized, put forward a few principles that the event extraction task to follow, such as abstraction principle, addition and deletion of information principle, principle of the importance of the preposition and alignment information principle, according to these principles we propose sentence ranking method, prepositions and adverbs characteristics. Through the contrast experiment to observe the influence of characteristics are given in this paper for the event extraction task, the experimental data prove that our method is effective.Finally, we design and implement a complete event extraction system for product requirement analysis. It is tested on the product information corpus. The experiment proves that our system can effectively accomplish the related event extraction task.