中文摘要：

文本内容通常包含多个侧面,全面地识别这些内容侧面对自然语言处理有重要地意义。传统的统计方法使用简单特征难以识别出所有的内容侧面。以自动摘要为例,传统的抽取式方法多以词频为主要特征,一些重要的句子常因重复度不高被舍弃。要想全面地覆盖原始文本的重要信息,就要识别出文本描述的内容侧面。该文以框架语义学为指导,使用FrameNet语料库作为知识库,综合多种特征来标注文本描述的框架,在此基础上识别文本所包含的内容侧面。该方法在新闻语料上取得了较好地结果,达到了61%的正确率。

英文摘要：

Texts usually contain various aspects of information.In natural language processing,many tasks would benefit from the recognition of these aspects.For example,in the summarization task,traditional method of extracting sentences is mainly bases on features of words frequency.Those sentences of great importance would be ignored if they appear infrequently.Aspect recognition can remedy this understanding defect..In this paper,we use FrameNet corpus as ontology to annotate sentences based on lexical and syntactical features.The frame of the annotated sentence explains its aspect information.This method works well when tested on the news corpus and the precision of aspect recognition based on frame information can reach 61%.