Text-based QA begins with question classification (e.g., yes-no questions, who-questions, what-questions, when-questions, etc.), followed by information retrieval for the identified type of question, followed by narrowing of the search to paragraphs and finally sentences that may contain the answer to the question. The successive narrowing typically employs word and other feature matching, and ultimately dependency and role matching, and perhaps limited textual inference to verify answer candidates. Textual inference may, for instance, use WordNet hypernym knowledge to try to establish that a given candidate answer sentence supports the truth of the declarative version of the question. Since the chosen sentence(s) may contain irrelevant material and anaphors, it remains to extract the relevant material (which may also include supporting context) and generate a well-formed, appropriate answer. Many early text-based QA systems up to 1976 are discussed in Bourne &amp; Hahn 2003. Later surveys (e.g., Maybury 2004) have tended to include the full spectrum of QA methods, but TREC conference proceedings  (https://trec.nist.gov/)  feature numerous papers on implemented systems for text-based QA.