Open Domain Question Answering (QA) Task
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An Open Domain Question Answering (QA) Task is a question answering task where there are few restriction on the domains of the factual questions being asked.
- Context:
- It can be supported by an Information Retrieval Task (such as Web searches).
- It can be solved by a Open-Domain QA System (that implements a Open-Domain QA algorithm).
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- Example(s):
- ...
- Counter-Example(s):
- See: TREC, Javelin, SQuASH System.
References
2023
- (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Question_answering#Types_of_question_answering Retrieved:2023-9-10.
- Question-answering research attempts to develop ways of answering a wide range of question types, including fact, list, definition, how, why, hypothetical, semantically constrained, and cross-lingual questions.
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- Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for open-domain question answering usually have much more data available from which to extract the answer. An example of an open-domain question is "What did Albert Einstein win the Nobel Prize for?" while no article about this subject is given to the system. ...
- Question-answering research attempts to develop ways of answering a wide range of question types, including fact, list, definition, how, why, hypothetical, semantically constrained, and cross-lingual questions.
2023
- (Wikipedia, 2023) ⇒ https://en.wikipedia.org/wiki/Question_answering#Open_domain_question_answering Retrieved:2023-9-10.
- 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. 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 extraction is the first step in identifying the input question type. 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. 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 tagger and 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 recogniser finds relevant "Person" and "Location" names from the retrieved documents. A vector space model can classify the candidate answers. Checkif 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."
- 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. 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 extraction is the first step in identifying the input question type. 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. 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.
2011
- http://en.wikipedia.org/wiki/Open_domain_question_answering
- In information retrieval, an open domain question answering system aims at returning 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. The system uses a combination of techniques from computational linguistics, information retrieval and knowledge representation for finding answers. 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?” The sentence is then transformed into a query through its logical form. Having the input in the form of a natural language question makes the system more user-friendly, but harder to implement, as there are various 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.
2006
- (Strzalkowski & Harabagiu, 2006) ⇒ Tomek Strzalkowski (editor), and Sanda M. Harabagiu (editor). (2006). “Advances in Open Domain Question Answering." Springer. ISBN:978-1-4020-4744-2
2003
- (Moldovan et al., 2003) ⇒ Dan Moldovan, Marius Paşca, Sanda Harabagiu, and Mihai Surdeanu. (2003). “Performance Issues and Error Analysis in an Open-Domain Question Answering System.” In: ACM Transactions on Information Systems (TOIS) 21(2). doi:10.1145/763693.763694