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Command and Natural Languages. Human Computer Interaction CIS 6930/4930 Section 4188/4186. Intro. Languages are a natural way to communicate Communication with systems Initially, programming languages Scripting languages Database query Command languages

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command and natural languages

Command and Natural Languages

Human Computer Interaction

CIS 6930/4930

Section 4188/4186

  • Languages are a natural way to communicate
  • Communication with systems
    • Initially, programming languages
    • Scripting languages
    • Database query
    • Command languages
  • With menus and DM, why have languages? For some tasks,
    • Natural
    • Faster
    • For tasks with many options, most effective
    • Small footprint (screen, power, size)
    • Logistics: Generating help, verification, etc.
  • Languages negatives?
    • User memory
    • Could be cryptic
    • Retention, learning, frustration
  • Ex. Web addresses
    • Class web page
  • Initiate vs. respond (ex. Unix)
functionality to support users task
Functionality to Support Users’ Task
  • People use systems to accomplish a task.
  • How do you build a command structure to support this?
  • Identify user tasks
    • Usually create 1 to 1 for functionality with actions and objects
    • Common error: Too many actions and objects
      • Overwhelms users
      • More code, more errors, more clutter
    • Insufficient actions: very frustrating!
functionality to support users task5
Functionality to Support Users’ Task
  • Create a list of tasks
    • Use a column for frequency of expected use
    • High frequency tasks should be easiest to remember and carry out
  • Careful thought into user base
    • Ex. do you need macros?
  • Transition diagram helps (Fig 8.1)
command organization strategies
Command-Organization Strategies
  • Strategies to create commands
  • Agreeing on a interface concept aides retention, learning, and problem solving
  • Not that straightforward
    • Ex. Load/Save, Read/Write (notes vs. folders), Open/Close (files vs. notes)
  • Common mistake: Choose a computer metaphor instead of a domain metaphor
    • Ex. e-mail
command organization strategies7
Command Organization Strategies
  • Simple Command Set
    • # of commands = # of tasks
    • Ex. MUDs
      • Ex. Look, go, move
    • Cons: Large # of commands
      • Ex. VI
  • Commands plus arguments/options
    • Each command is followed by >=0 arguments
      • Ex. Copy X Y
      • Include keyword labels: Copy From=X To=Y
    • Pros: readability, fewer semantic errors, better for novices
    • Cons: increased syntax errors, slower for experts
  • Hierarchical command structure
    • Tree structure of commands (like menus)
    • Let’s create one for files
      • Create,display,remove,copy, move
      • File, process, directory
      • File, printer, screen
    • Easy to write tutorials
benefits of structure
Benefits of Structure
  • Study: Error rates for UNIX
    • 3 to 53% (Hanson ’84)
    • Common commands too! (18% for mv, 30% for cp)
    • Experts gain some (perhaps sadistic) fulfillment and club ‘inclusion’ by understanding complex command languages
  • Benefits
    • Learning
    • Memory
    • Problem solving
  • Elegancy vs. Consistency
    • Apply ‘edit’ vs. ‘revise, change, replace’, etc.
    • Reduces error
  • Other examples
    • Some commands are two characters, others not
    • What is a binary decision? On/Off, True/False, etc.
    • Multiple design groups
  • Solution: Create a guidelines document. Good for managers and designers
benefits of structure9
Benefits of Structure
  • Study: Benefits to argument ordering consistency (Barnard ’81)
    • Ex. Source or ID is always a certain argument
  • Symbols vs. Keywords
    • Which is better: FIND:/TOOTH/;-1 or BACKWORD TO “TOOTH”
    • What about for different grade of users? (Novice, Familiar, Expert)?
    • Study: Table 8.1 (Ledgard ’80)
    • Clarity overrides speed
  • Study: (Carroll ’82)
    • Effect of congruency [meaningful pairs] and hierarchies on performance
    • Ex. Open/Close Left/Right
    • Memory and problem solving improved w/ congruency
    • Error rates reduced w/ congruent hierarchy
    • Results:
      • Congruency = very good
      • Hierarchy = good for large command sets
  • Good things to have: positional and grammatical consistency, congruent pairing, hierarchical form
naming and abbreviations
Naming and Abbreviations
  • Let’s look at UNIX
    • mkdir (make directory)
    • ls (list directory)
    • cd (change directory)
    • rm (remove file)
    • pwd (print working directory)
  • What’s wrong with these abbreviations?
    • No standard method to derive them!
    • Standards are important aid
specificity vs generality
Specificity vs. Generality
  • Specific – more descriptive
  • General – more familiar and easier to understand
  • Study: 2 week training session
    • Resulted in specific > general (Barnard ’81)
  • Study: (Black and Moran ’82) – pg. 328. Different terms for insert/delete
    • Infrequent, discriminating: insert/delete
    • Frequent, discriminating: add/remove
    • Infrequent, nondiscriminating: amble/perceive
    • Frequent, nondiscriminating: walk/view
    • General: alter/correct
    • Nondiscriminating nonwords: GAC/MIK
    • Disciminating nonwords: abc-adbc/abc-ac
    • Best = infrequent, discriminating words
    • Worst – general
    • Not bad – nonsense
    • What does this teach us? (distinctive-ness is a plus)
abbreviation strategies
Abbreviation Strategies
  • Should be easy to express with input device
    • Keyboard, pen (PDA), speech recognition, mouse
  • Error rates increase w/ more complex commands
    • Shift, Ctrl (plus harder for disabled or motor-damaged users)
    • Brevity is good, but must weigh w/ retention and learning
    • Study: (Landauer ’83) novices don’t mind typing out full names [increases confidence] (<5 to 7 uses)
  • Abbreviation Strategies
    • Simple truncation – commands must be distinguishable
    • Vowel drop
    • First and last letter
    • First letter of each word
    • Standard abbreviations – familiarity
    • Phonics – XQT
abbreviation guidelines
Abbreviation Guidelines
  • Simple primary rule
  • Secondary rule abbreviations should be denoted by some distinguishing character
  • Minimal use of secondary rule
  • Users should know the rules
  • Truncation should be used, except when too many similar actions
  • Fixed-length is preferable to variable length
  • Computer generated messages should NOT use abbreviations
  • Should be greater than >2 savings for abbreviations
  • Consider a command menu.
    • Ex. Imaging Control [really benefits only intermittent users]
  • Underscore critical letter (like in Windows)
natural languages in computing
Natural Languages in Computing
  • One (popular) trend is to communicate with the computer using natural languages
    • This involves both input and output
  • Why is this hard?
    • Subtleties (mood, accent, culture)
    • Context sensitive
    • Large user base
  • Currently:
    • Very restricted domains (stock trading phone system)
    • Processed input and/or output
    • Formatted texts (weather reports, tech reports, etc.)
    • Can’t do: poems, freeform conversations
    • Rough translations help w/ getting the ‘jist’ of most things
      • Ex. language learners
natural language interaction
Natural Language Interaction
  • NLI – Star Trek-type cognition
  • Pros:
    • Don’t have to remember syntax or menu conventions
  • Cons: (besides harder)
    • Not necessarily faster
    • Not necessarily a goal for every type of app.
      • Ex. Air traffic control
      • Not knowing the extent of capabilities hampers novice or intermittent
      • Experts like precise commands
      • Data input/output types and rates vary greatly! 1:1000
  • Combine with the OAI model and provide a visual representation of options
  • Overzealousness is hampering
  • How can a system handle the high error rates with most NLI?
natural language interaction16
Natural Language Interaction
  • Ex. Use NLI for finances (Shneiderman ’80)
    • ‘Pay $33 to University of Florida’
    • 91% accuracy
    • Why isn’t it used now?
      • Quicken, et. al., doesn’t use NLI
      • Faster, easier to understand, visuals help
  • Loebner Prize (’91) – Turing Test
    • (
    • researchers aren’t that enthusiastic
  • Mainstream – HAL, ELIZA
  • Current:
    • Dialog interaction is too difficult
    • Rigorous evaluation of NLI
    • Identify keywords in documents
    • Visual recognition is just faster
    • Speech Rec
      • Problems: Predictable responses
  • Summary: sometimes developers believe NLI should operate w/o Direct Manipulation. This would be a mistake for many apps
natural language queries and question answering
Natural Language Queries and Question Answering
  • Instead of full NLI, look at a subset
    • Natural Language Queries
      • Easier to parse
      • Ex. AskJeeves
      • If input to a database, it could be constrained enough
    • But is it better than SQL?
      • Study: SQL was faster (Small ’83, Jarke ’85)
  • Case study: INTELLECT
    • Search financial mainframe databases in the 80s)
    • 400 installations
    • Text input for query
    • Helps because keywords are well defined (like cities)
    • Used fields to help structure input
    • Used structured output to help train users on structured input
    • Novices still had a hard time, ideal user: knowledgeable intermittent user
natural language queries and question answering18
Natural Language Queries and Question Answering
  • Other products:
    • Symantec’s Q&A (late 80s)
    • Microsoft’s English Query (’99)
  • NLQA (Answering)
    • Return a set of potential answers
      • Instead of an natural language answer
      • Reduce accuracy of response
      • Let the user hunt
    • Requires users to be domain knowledgeable
    • Domain of search could make things difficult (terms like year or pay)
    • Questions need to be well formed (not guaranteed)
text database searching
Text-Database Searching
  • Text-Database searching using NLQ
    • Court documents
    • Photo/multimedia
    • News
  • Spectrum of approaches
    • Understanding Query
      • Finding synonyms
      • Reduce noise words
      • Handling singulars vs. plurals (stemming)
      • Misspellings, pronouns, specific words
    • Extraction
      • Breaks down query into fields, does typical database lookup
      • Good for large databases (legal, medical, etc.) with formatted queries
    • Study: (Voorhees ’02), NLQ seems to provide rapid learning and progress
    • Provide more relevant searches vs. just keywords
    • Still not returning exact search result
    • Potentially faster (ex. user has partial information)
natural language text generation
Natural Language Text Generation
  • Prepare structured reports using NL
  • Goal: create stories?
  • Sports game recaps, wills
  • What’s the source?
    • Database
    • Interactive system
  • Natural language could help doctors (they don’t want to switch gaze)
adventure games and instructional systems
Adventure Games and Instructional Systems
  • Recall old Zork or King’s Quest games?
    • Problems: didn’t get the phrasing just right…
    • Pros: The ‘exploration’ is a plus since it aids to the experience
    • Cons: Too much exploration is frustrating
  • Instructional Tutorials
    • AutoTutor (Glassner) pg. 340
      • Uses agents to help students
      • A better interface for learning?
    • Cognitive Tutor (Carnegie Learning)
      • Teach math, geometry, algebra, etc.
    • Provide feedback and guidance w/ NL using accepted pedagogy approaches
      • Helps students (Study: Di Eugenio ’02)