This paper describes machine learning based parsing and question classification for question answering. We demonstrate that for this type of application, parse trees have to be semantically richer and structurally more oriented towards semantics than what most treebanks offer. We empirically show how question parsing dramatically improves when augmenting a semantically enriched Penn treebank tr...

Question answering systems can be seen as the next step in information retrieval, allowing users to pose questions in natural language and receive succinct answers. In order for a question answering system as a whole to be successful, research has shown that the correct classification of questions with regards to the expected answer type is imperative. Question classification has two components...

The queries to a practical Question Answering (QA) system range from keywords, phrases, badly written questions, and occasionally grammatically perfect questions. Among different kinds of question analysis approaches, the pattern matching works well in analyzing such queries. It is costly to build this pattern matching module because tremendous manual labor is needed to expand its coverage to s...