Natural-Language Interfaces

1986 ◽  
Vol 1 (1) ◽  
pp. 47-82 ◽  
Author(s):  
C R Perrault ◽  
B J Grosz
Keyword(s):  
Natural Language ◽  
Natural Language Interfaces

Natural language interfaces using limited semantic information

1982 ◽  
Author(s):  
Ralph Grishman ◽  
Lynette Hirschman ◽  
Carol Friedman
Keyword(s):  
Natural Language ◽  
Semantic Information ◽  
Natural Language Interfaces

Integrating Linguistic and World Knowledge for Domain-Adaptable Natural Language Interfaces

2014 ◽  
pp. 237-241
Author(s):  
Hen-Hsen Huang ◽  
Chang-Sheng Yu ◽  
Huan-Yuan Chen ◽  
Hsin-Hsi Chen ◽  
Po-Ching Lee ◽  
...  
Keyword(s):  
Natural Language ◽  
World Knowledge ◽  
Natural Language Interfaces

Natural language interfaces

1978 ◽  
Author(s):  
Howard Lee Morgan ◽  
Edgar F. Codd ◽  
William A. Martin ◽  
Larry Harris ◽  
Daniel Sagalowicz ◽  
...  
Keyword(s):  
Natural Language ◽  
Natural Language Interfaces

scholarly journals Empowering Natural Language Interfaces to Databases with Aggregations

2021 ◽  
Vol 12 (5) ◽  
Author(s):  
Alexandre F. Novello ◽  
Marco A. Casanova
Keyword(s):  
Natural Language ◽  
Relational Database ◽  
Natural Language Interfaces ◽  
Aggregation Functions ◽  
Natural Language Interface ◽  
Measurement Units ◽  
Database Interface ◽  
Multiple Attributes ◽  
Generic Module ◽  
Query Aggregation

A Natural Language Interface to Database (NLIDB) refers to a database interface that translates a question asked in natural language into a structured query. Aggregation questions express aggregation functions, such as count, sum, average, minimum and maximum, and optionally a group by clause and a having clause. NLIDBs deliver good results for standard questions but usually do not deal with aggregation questions. The main contribution of this article is a generic module, called GLAMORISE (GeneraL Aggregation MOdule using a RelatIonal databaSE), that extends NLIDBs to cope with aggregation questions. GLAMORISE covers aggregations with ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic recognition of measurement units, and aggregations in attributes with compound names.

Welcome to AI Matters 7(2)

AI Matters ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. 3-4
Author(s):  
Iolanda Leite ◽  
Anuj Karpatne
Keyword(s):  
Natural Language ◽  
Regular Education ◽  
Neural Models ◽  
Natural Language Interfaces ◽  
Algorithmic Bias

Welcome to the second issue of this year's AI Matters Newsletter. We start with a report on upcoming SIGAI Events by Dilini Samarasinghe and Conference reports by Louise Dennis, our conference coordination officer. In our regular Education column, Carolyn Rosé discusses the role of AI in education in a post-pandemic reality. We then bring you our regular Policy column, where Larry Medsker covers interesting and timely discussions on AI policy, for example whether governments should play a role in reducing algorithmic bias. This issue closes with an article contribution from Li Dong, one of the runner-ups in the latest AAIS/SIGAI dissertation award, on the use neural models to build natural language interfaces.

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