A PATTERN MATCHING METHOD FOR EXTRACTING PERSONAL INFORMATION IN FARSI CONTENT
Personal information extraction as a subtask of entity profiling is the process of identifying and extracting useful and structured information of the focus person named entity from the content related to it. This is a demanding task, so it is of great importance of how to extract the personal information in a given text. In this paper, we present an approach to extract the relevant information of the focus persons in Farsi content. Our approach contains three steps, pre processing, attribute extraction and cross document profile fusion. In pre processing step, we prepare the input text as system's desired format using some existing text processing tools. In attribute extraction phase, we identify and extract all the related attributes of the target persons in a given text. To fulfill this aim, we use a pattern matching approach. This method contains a set of keyword based patterns and manually collected pre compiled attribute value candidate lists. In cross document profile fusion phase, we integrate the distributed information of the focus persons from multiple documents and resolve ambiguity between person names. We evaluate our approach on a sample Farsi textual corpus drawn from news Websites and Wikipedia articles. The experimental results show that our approach is capable to extract personal information, thereby laying the foundation for person named entity profiling in Farsi.