Enhancing Human-Computer Interaction through Natural Language Processing

1. Unit 3: Interacting with ComputersNatural Language Denise Allen dallen@utech.edu.jm or denizallen@yahoo.co.uk

2. Learning Objective • Natural Language in Computing • Principles and Guidelines in Interface Design

3. Basic Goals of Language Design • Precision • Compactness • Ease in writing and reading • Completeness • Speed in learning • Simplicity to reduce errors • Ease of retention over time

4. Constraints on a Language • The capacity for human beings to record the notation • The convenience in speaking and typing

5. Language Development • The printing press made widespread dissemination of written work possible • The computer widespread dissemination through networks • Early computers were built to perform mathematical computations • Quickly found to be effective manipulators of logical expressions, business data, graphics, sound and text. • Operating on the real world directing robots, guiding spacecraft, etc.

6. Natural Language in Computing • Even before there were computers, people dreamed about creating machines that would accept natural language. • Although there has been some progress in machine translation from one language to another, most effective systems require constrained or preprocessed input or post processing of output. • High quality translations of complete documents without human intervention seem difficult to attain.

7. Natural Language in Computing • Language is subtle, contexts are complex and emotional relationships have a powerful and pervasive effect on H-2-H comm. • Full comprehension and language generation seems inaccessible, however, many ways still exists for computers to deal with NL. • Interaction • Queries • Database searching • Text generation • Adventure games

8. Natural Language Interaction • Researchers hope to fulfill the Star Trek scenario, where computers will respond to commands users issue by speaking.

9. Natural Language Interaction • The operation of computers by people using a familiar natural language to give instructions and receive responses. • Users do not have to learn a command syntax or to select from menus

10. Natural Language Interaction – Problems? • Implementation • Desirability for large number of users for a wide variety of tasks. • People are different from computers, and human-human interaction is not necessarily an appropriate model for human operation of computers.

11. Natural Language Interaction – Problems? • Since computers can display info. 1000 x faster than people can enter commands it seems advantageous to use the computer to display large amounts of info. And to allow novice and intermittent users, simply to choose among the items. Selection helps to guide the user by making clear what functions are available

12. Natural Language Interaction • Most designs for NLI do not provide information about task actions and objects; users are usually presented with a simple prompt that invites a natural language statement. • NLI designs should relieve users who are knowledgeable about specific tasks and interface concepts but who are intermittent users who cannot retain the syntactic details of the interface.

13. Natural Language Interaction • Ford(1981) created and tested a textual NLI system where users were paid to maintain their checkbook registers by computer using an APL-based program that was refined incrementally to account for unaccountable entries

14. Natural Language Interaction • The system successfully handled 91% of user’s request such as these: • Pay to Safeway on 3/24/86 $29.75. • Show me all the checks paid to George Bush. • Which checks were written on October 29? • Yet, direct-manipulation alternatives have proved more attractive in the marketplace. In short, pointing and selecting in context is often more attractive than is typing or even speaking an English sentence.

15. Natural Language Queries • Some designers have pursued a more-limited goal of natural-language queries (NLQ) since general interaction is difficult to support. • E.g. INTELLECT – 400 installations on large mainframe computers during the 80s • First, the parser used the contents of the database to parse queries; e.g. the parser could determine that a query containing Cleveland referred to city locations. • It also rephrased the user’s query and displayed a response such as PRINT THE CHECK NUMBERS WITH PAYEE = GEORGE BUSH.

16. Natural Language – Other Uses • Text-database searching – uses filters and parsers for queries expressed in natural language e.g. (‘Find cases of tenants who have sued landlords unsuccessfully for lack of heat’). • Example systems Q & A, • Some search engines such as Lycos, Alta Vista, Infoseek) use modest NL techniques.