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Communicating with Databases in Natural Language - Wallace M.

Wallace M. Communicating with Databases in Natural Language - Ellis Horwood Limited, 1985. - 170 p.
Download (direct link): comumunicatingwthisdatabase1985.djvu
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Perhaps an example would be of interest, NLUs generally treat the question-word “who” as a request for a person (or employee, manager, etc.). However, in different contexts “who” can mean “which company?”:
“Who oidered 10,000 widgets?” or “which country?”:
“Who supported Britain at the UN?”
To deal with “who” a genuinely portable NLU therefore requires at least the rudiments of a preference semantics.
An NLU that is effortlessly transferred will be correspondingly restricted in its understanding of English.
Pragmatics is the term for the common sense aspects of answering questions. The application of pragmatics can be dissected into two parts:
(1) Intelligent understanding.
(2) Generating pertinent responses.
11.1 Intelligent understanding
An NLU works its way through an English query following a set of grammatical rules and guided by a set of semantic restrictions until at last it yields a formal
queiy. Sometimes, however, no answer can be found; and sometimes two or more can
In these cases the NLU has to be a bit cleverer than usualf 1111 No answers found
When LIFER fails to parse a sentence it resorts to a spelling correction. If this fails it assumes that ellipsis is taking place
In general, any NLU will have to relax the rules of syntax and semantics that were initially applied. The facility to be satisfied with fewer and fewer preferences is a particularly powerful feature of Wilks’s approach!,
11.1.2 Ambiguity
Many systems can deal effectively with too many meanings, on the other hand The REL solution is to try out the queries on the database and see which do not yield a null response, SHRDLU’s resolution of definite noun-phrases (by finding the most recently mentioned N objects which satisfy the description), embodies the same approach in order to satisfy the presuppositions of the definite determiner.
Pragmatics do have a significant part to play in guessing the correct quantifier hierarchy. In Woods’s example;
“List the departure times from Boston of every American Airlines flight”,
the quantifier “every” was inserted in front of the rest of the query in the formal MRL representation. If the first noun-phrase was singular as in;
“Who is the man behind all our research projects?”
then the quantifier “all” should not be swapped around.
One other use of pragmatics in resolving ambiguity is employed in the railway booking clerk system of Perrault [35], A client might ask the clerk
“What time does the Boston train get in?”
“What times does the Boston train leave?”
Clearly, “Boston train” is ambiguous as between “train from Boston” and “train to Boston”, The system removes the ambiguity from “Boston train” on the basis
f Of course systems do incorporate pragmatic principles in the parser itself, but it clarifies the discussion if such common-sense principles are treated separately % Wilks’s semantics does allow meaningless sentences if forced by the syntax However, the preference system should, after successful parsing, raise a flag to say that there is something wrong. Otherwise the formal query will simply cause a database error “Was Dept X born on 15 3 80?”
(Database Error)
of the intentions imputed to the client Thus when the client asked what time the Boston train left, he was probably intending to travel to Boston, so he must have meant the train to Boston when he said “Boston train”
The use of pragmatics in natural language understanding is guided by Grice’s conversational postulates and implications [17]. There is not, of course, anything approaching a set of algorithms for dealing with the pragmatics of conversation and it may well be premature to create NLUs that reproduce a few such pragmatic features before basic rules of meaning have even been formalised
11.2 Generating pertinent f responses
The use of pragmatics in generating responses from a database is essential SHRDLU distinguished three levels of detail — “how-many”, “vague”, “specific”
— appropriate to three kinds of question word — “how many , ”, “what” and
“which .” respectively. Furthermore SHRDLU avoided repeating in the answer anything already present in the question, thus the query:
“Is there a red cube which supports a pyramid?”
was answered
“Yes, a large one”
rather than
“Yes, a large red cube”.
The same pragmatic rule can enable a system to distinguish a question about meaning from a question about the data itself. Contrast;
“What is the part required by client X?”
“What is a P140?”
The answer to the first may be “a P140”, but this could not be the answer to the second. The second question must be a question about “meta-data”: meaning, or organisation, of the data.
It is not, in principle, hard to include some meta-data in the database. The problem, for the NLU, is how to distinguish meta-questions from ordinary ones SHRDLU’s simple pragmatic rule is a good start
A frequent problem in generating answers is how to avoid giving a long list of items which match a certain description. An approach used in SHRDLU, and also currently being developed at Stanford University [25] is to group items in a
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