1 Motivation Question Answering has emerged as a key area in natural language processing (NLP) to apply question parsing, information extraction, summarization, and language generation techniques (Clark et al. , 2004; Fleischman et al. , 2003; Echihabi et al. , 2003; Yang et al. , 2003; Hermjakob et al. , 2002; Dumais et al. , 2002). Several QA systems have responded to these changes in the nature of the QA task by incorporating various knowledge resources (Hovy et al. , 2002), handling of additional types of questions tapping into external data sources such as web, encyclopedia, and databases in order to find the answer candidates, which may then be located in the specific corpus being searched (Xu et al. , 2003). 2 Related Work Question Answering has attracted much attention from the areas of Natural Language Processing, Information Retrieval and Data Mining (Fleischman et al. , 2003; Echihabi et al. , 2003; Yang et al. , 2003; Hermjakob et al. , 2002; Dumais et al. , 2002; Hermjakob et al. , 2000). We use our existing QA system (Hovy et al. , 2002b; 2001) to do so. Open-Domain Question Answering System (QA) has gain great popularities among scholars who care the above problem (Li, et al. 2002; Moldovan, et al. 2003; Zhang, et al. 2003), for QA can meet users demand by offering compact and accurate answers, rather than text with corresponding answers, to the questions presented in natural language.