Cohesion and Learning in a Tutorial Spoken Dialog System

Measuring Cohesion

• Measurements from Computational Linguistics
• Hearst(94) topic segmentation, text
• Word-count similarity of spans of text
• Olney & Cai (05) topic segmentation, tutorial dialog
• Several measures, including Hearst’s
• Morris & Hirst (91) Lexical Chains
• Thesaurus entries
• Barzilay & Eldihad (97) Automatic Lexical Chains
• WordNet senses
• We develop measures similar to Hearst’s
• But novel in that:
• Applied to dialog rather than text,
• used to find correlations with learning

Issue 1: How identify cohesion in dialogs?

• Defining Cohesion
• Halliday and Hassan (76)
• Grammatical vs Lexical Cohesion
• Lexical Cohesion
• Reiteration
• Exact word repetition
• Synonym repetition
• Near Synonym repetition
• Super-ordinate class
• General referring noun
• Cohesion measured by counting “cohesive ties”
• Two words joined by a cohesive device (i.e. reiteration)

Issue 1: How identify cohesion in dialogs?

• Defining Cohesion
• Halliday and Hassan (76)
• Grammatical vs Lexical Cohesion
• Lexical Cohesion
• Reiteration
• Exact word repetition
• Synonym repetition
• Near Synonym repetition
• Super-ordinate class
• General referring noun
• Cohesion measured by counting “cohesive ties”
• Two words joined by a cohesive device (i.e. reiteration)

Issue 1: How identify cohesion in dialogs?

• How we measure Lexical Cohesion
• We count cohesive ties between turns
• Tokens (with stop words)
• (token = “word”)
• Tokens (stop words removed)
• (Stops = high frequency, low information words)
• Stems (stop words removed)