Editing Question answering (section)

===Open domain question answering===
{{more citations needed section|date=January 2016}}
In [[information retrieval]], an open-domain question answering system tries to return an answer in response to the user's question. The returned answer is in the form of short texts rather than a list of relevant documents.<ref>{{cite book |last1=Sun |first1=Haitian |last2=Dhingra |first2=Bhuwan |last3=Zaheer |first3=Manzil |last4=Mazaitis |first4=Kathryn |last5=Salakhutdinov |first5=Ruslan |last6=Cohen |first6=William |title=  Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing|chapter=Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text |date=2018 |pages=4231–4242 |chapter-url=https://www.aclweb.org/anthology/D18-1455 |location=Brussels, Belgium|doi=10.18653/v1/D18-1455 |arxiv=1809.00782 |s2cid=52154304 }}</ref> The system finds answers by using a combination of techniques from [[computational linguistics]], [[information retrieval]], and [[knowledge representation]].

The system takes a [[natural language]] question as an input rather than a set of keywords, for example: "When is the national day of China?" It then transforms this input sentence into a query in its [[logical form]]. Accepting natural language questions makes the system more user-friendly, but harder to implement, as there are a variety of question types and the system will have to identify the correct one in order to give a sensible answer. Assigning a question type to the question is a crucial task; the entire answer extraction process relies on finding the correct question type and hence the correct answer type.

Keyword [[Data extraction|extraction]] is the first step in identifying the input question type.<ref>{{cite book |last1=Harabagiu |first1=Sanda |last2=Hickl |first2=Andrew |chapter=Methods for using textual entailment in open-domain question answering  |title= Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06 |date=2006 |pages=905–912 |doi=10.3115/1220175.1220289 |chapter-url=https://www.aclweb.org/anthology/P06-1114 |doi-access=free }}</ref> In some cases, words clearly indicate the question type, e.g., "Who", "Where", "When", or "How many"—these words might suggest to the system that the answers should be of type "Person", "Location", "Date", or "Number", respectively. [[part-of-speech tagging|POS (part-of-speech) tagging]] and syntactic parsing techniques can also determine the answer type. In the example above, the subject is "Chinese National Day", the predicate is "is" and the adverbial modifier is "when", therefore the answer type is "Date". Unfortunately, some interrogative words like "Which", "What", or "How" do not correspond to unambiguous answer types: Each can represent more than one type. In situations like this, other words in the question need to be considered. A lexical dictionary such as [[WordNet]] can be used for understanding the context.

Once the system identifies the question type, it uses an [[information retrieval]] system to find a set of documents that contain the correct keywords. A [[Part-of-speech tagging|tagger]] and [[Phrase chunking|NP/Verb Group chunker]] can verify whether the correct entities and relations are mentioned in the found documents. For questions such as "Who" or "Where", a [[named-entity recognition|named-entity recogniser]] finds relevant "Person" and "Location" names from the retrieved documents. {{clarify|reason=who selects? who ranks? what does "ranking" mean in this context?|text=Only the relevant paragraphs are selected for ranking.|date=April 2023}}

A [[vector space model]] can classify the candidate answers. Check{{Who|date=April 2023}} if the answer is of the correct type as determined in the question type analysis stage. An inference technique can validate the candidate answers. A score is then given to each of these candidates according to the number of question words it contains and how close these words are to the candidate—the more and the closer the better. The answer is then translated by parsing into a compact and meaningful representation. In the previous example, the expected output answer is "1st Oct."