P05-2015 three prior systems on a standard story comprehension corpus . <title> Dependency-Based
P05-2015 specifically addressed the task of story comprehension . The Deep Read reading comprehension
J10-1003 research tackling the problem of story comprehension . During the 1970s and 1980s
J10-1003 body of research in automatic story comprehension . Section 3 describes the process
J00-3005 breaks play an important role in story comprehension . In my own experiments ( see
C90-3065 nonlinguistic knowledge are required for story comprehension . Ironically , this very type
W09-3708 System Components Similar to many story comprehension systems ( e.g. -LSB- 8 -RSB-
J10-1003 patterns . Research in automatic story comprehension offered a number of important
J07-2006 Narayanan 's ( 1997 ) model of story comprehension . In this model , background
D13-1020 compendium of resources related to the story comprehension task , see Mueller ( 2010 ) .
P05-2015 text-based question answering known as story comprehension . Most TREC-style QA systems
P05-2015 particularly acute in the case of story comprehension due to the rarity of information
W98-0312 upon psycholinguistic studies of story comprehension ( Graesser and Clark , 1985 )
P83-1010 frame-like representation for both story comprehension and problem-solving . The system
P05-2015 learning inference procedures for story comprehension through inductive generalization
P79-1003 the specific domain at hand . In story comprehension , this involves the plot , characters
T75-2036 . Investigating the problem of story comprehension via conceptual overlays and CSA
P98-2249 Cognitive Model of Coherence-Driven Story Comprehension </title> Elliot Smith Abstract
C02-1151 ( QA ) ( Voorhees , 2000 ) and story comprehension ( Hirschman et al. , 1999 ) .
W11-0608 tasks , followed by the off-line story comprehension results , i.e. , answers on the
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