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C hrono S earch

Pete Bohman Adam Kunk. C hrono S earch. ChronoSearch. ChronoSearch : A System for Extracting a Chronological Timeline. C h r o n o. Motivation. Current search engines do not provide a complete picture Latest events dominate top results

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C hrono S earch

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  1. Pete Bohman Adam Kunk ChronoSearch

  2. ChronoSearch • ChronoSearch: A System for Extracting a Chronological Timeline Chrono

  3. Motivation • Current search engines do not provide a complete picture • Latest events dominate top results • The user is forced to parse through lots of pages to find a complete list of information • ChronoSearch aims to summarize search results into a concise list of important events related to an entity

  4. Problem Definition • Input: An entity E (most likely a person) • Output: A sorted list of events, L, which are related to E L = { li| li is unique and li occurred before li+1}

  5. Problem Statement • Tuple extraction: (Event, Entity, Date) • Difficulties of Extraction • Dates • No standard format, relative dates • Events • Hard due to random input, unstructured data • Entity • Pronouns (“He” / “She”) • Entity Event Association

  6. Our Approach • Baseline Approach – Web Redundancy • Date extraction based on absolute dates • Entity extraction by literal entity • Association based on sentence boundary • Event is implicitly described by the sentence itself • We consider sentences containing the entity being searched as well as an absolute time

  7. Our Approach • Baseline Approach • Leverages Web Redundancy

  8. Initial Results • Demo time…

  9. Results Analysis • Information Retrieval (IR) performance characteristics: • Precision – fraction of documents retrieved that are relevant to query • Recall – fraction of documents that are relevant to query that are successfully retrieved

  10. Ultimate Approach • Improving precision: • (Part 1) Eliminating duplicates • (Part 2) Eliminating unimportant results

  11. Eliminating Duplicates • Improving precision: • (Part 1) Eliminating duplicates • Cosine similarity duplicate detection • The probability that s and s’ are the same event: • P(s' reports the same event as s) = cosine( s ' ,s ) • Term frequency vectors: s and s ’

  12. Eliminating Unimportant Results • Improving precision: • (Part 2) Eliminating unimportant results • Important results occur more frequent • Utilize term frequency to eliminate unimportant events • Option 1: Term frequency calculations based on results returned from initial search query • Results that do not occur frequently in the returned corpus will be eliminated • Option 2: Leverage Google search

  13. Eliminating Unimportant Results Cont. • Eliminate results outside of “-x” standard deviations based on search results returned for the given result

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