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Title: Resolution of time in natural language and its application to text summarization
Researcher: Sanampudi, Suresh Kumar
Guide(s): Vijaya Kumari, G
Keywords: Natural Language Text
Multi-Document Summarization
Computer Science
Upload Date: 3-Sep-2012
University: Jawaharlal Nehru Technological University
Completed Date: October, 2010
Abstract: Automatic recognition of temporal expressions in natural language text is an active area of research in Artificial Intelligence (AI) and Computational Linguistics. Temporal information is the level of information expressing the time of events occurrences described in the text. It conveys the end-points, durations and intervals of events occurrences that happen in the real world. The events may be as significant as ?World War II? or as mundane as ?Boarding the Bus?. Though temporal information representation and reasoning have been widely discussed in AI, They have started receiving greater attention in the area of Natural Language Processing (NLP).Extraction and representation of time expressions are necessary components for reasoning about occurrences of events in natural language text as the information derived from event times impose a chronological ordering of the events described in the text. It is also essential for any task requiring resolution and positioning of events on timeline.The aim of the dissertation is to design a framework for extraction, representation and reasoning with temporal events in natural language text. Event times cannot always be expressed on absolute scale and are inter dependent on other events in the text. Inclusion of temporal relations between events in the model is therefore fundamental. Thus the framework has been devised to be capable of representing all possible temporal information about events such as end-points, durations, and inter-event relations inorder to facilitate reasoning with them. The properties of a general specification language to markup temporal and event information in text are investigated. The role of the devised framework in the development of robust text summarization systems has been investigated.In text summarization systems, extractive methods especially, identify and sequence the sentences which involve significant events.
Pagination: 130p.
Appears in Departments:Faculty of Computer Science & Engineering

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01_title.pdfAttached File177.74 kBAdobe PDFView/Open
02_certificate.pdf161.99 kBAdobe PDFView/Open
03_declaration.pdf128.25 kBAdobe PDFView/Open
04_acknowledgements.pdf171.59 kBAdobe PDFView/Open
05_abstract.pdf168.43 kBAdobe PDFView/Open
06_table of contents.pdf151.99 kBAdobe PDFView/Open
07_list of figures.pdf172.37 kBAdobe PDFView/Open
08_list of tables.pdf134.62 kBAdobe PDFView/Open
09_chapter 1.pdf265.2 kBAdobe PDFView/Open
10_chapter 2.pdf292.39 kBAdobe PDFView/Open
11_chapter 3.pdf349.72 kBAdobe PDFView/Open
12_chapter 4.pdf374.94 kBAdobe PDFView/Open
13_chapter 5.pdf436.61 kBAdobe PDFView/Open
14_chapter 6.pdf267.95 kBAdobe PDFView/Open
15_chapter 7.pdf381.17 kBAdobe PDFView/Open
16_chapter 8.pdf378.79 kBAdobe PDFView/Open
17_chapter 9.pdf456.85 kBAdobe PDFView/Open
18_chapter 10.pdf138.02 kBAdobe PDFView/Open
19_references.pdf282.61 kBAdobe PDFView/Open
20_appendix.pdf547.42 kBAdobe PDFView/Open

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