A temporal analysis of natural language narrative text
Written English texts in the form of narratives often describe events that occur in definite chronological sequence. Understanding the concept of time in such texts is an essential aspect of text comprehension and forms the basis for answering time related questions pertaining to the source text....
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| Format: | Others |
| Language: | en |
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Virginia Tech
2014
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| Online Access: | http://hdl.handle.net/10919/41466 http://scholar.lib.vt.edu/theses/available/etd-03122009-040648/ |
| Summary: | Written English texts in the form of narratives often describe events that occur in definite
chronological sequence. Understanding the concept of time in such texts is an essential aspect
of text comprehension and forms the basis for answering time related questions pertaining
to the source text. It is our hypothesis that time in such texts is expressed in terms of
temporal orderings of the situations described and can be modelled by a linear representation
of these situations. This representation conforms to the traditional view of the linearity of time
where it is regarded as a horizontal line called the timeline.
Information indicating the temporal ordering of events is often explicitly specified in the source
text. Where such indicators are missing, semantic relations between the events enforce
temporal orderings. This thesis proposes and implements a practical model for automatically
processing paragraphs of narrative fiction for explicit chronological information and employing
certain guidelines for inferring such information in the absence of explicit indications. Although
we cannot claim to have altogether eliminated the need for expensive semantic inferencing
within our model, we have certainly devised guidelines to eliminate the expense in
certain cases where explicit temporal indicators are missing. We have also characterized
some cases through our test data where semantic inferencing proves necessary to augment
the capabilities of our model. === Master of Science |
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