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Information Extraction

In this text extraction example, the user expresses a desire to meet with John on Tuesday at the Mattin Center coffee shop to discuss coffee consumption policies within the computer science department. The meeting is preferred to take place in the morning. The process involves creating rules for templates to streamline information extraction, making it easier to gather specific meeting details such as partners, dates, locations, and topics of discussion. Various techniques for pattern extraction and semantic role assignment are also explored.

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Information Extraction

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  1. Information Extraction

  2. Extracting Information from Text System : When would you like to meet Peter? User : Let’s see, if I can, I’d like to meet him on Tuesday.

  3. Template Filling Partner: John Day: Tuesday Location: Mattin Ctr Topic: Coffee Time of Day: Morning I’d like to meet John on Tuesday in the Mattin Center coffee shop. We should discuss policies on coffee consumption in the computer science department. Anytime in the morning would be fine.

  4. Template Filling I’d like to meet John on Tuesday in the Mattin Center coffee shop. We should discuss policies on coffee consumption in the computer science department. Anytime in the morning would be fine. Partner: John Day: Tuesday Location: Mattin Ctr Topic: Coffee Time of Day: Morning Or should this be 8am-12pm?

  5. How do we write general rules? • Finite State Machines • (regular expressions) • Extraction from partial parses • Full Parsing

  6. Rules with Assigned Semantic Roles Meet with <partner> on <date>. On <date> I want to meet <partner>. Many patterns fill the same template slots.

  7. How can we write these rules? • Manually enumerate them? • <company> manufactures <product> • Any other ways to automatically rewrite this?

  8. Semantic Lexicons <company> manufactures <product> Laughter manufactures happiness. How could we avoid this problem?

  9. Can we learn them? I’d like to meet John on Tuesday in the Mattin Center coffee shop. We should discuss policies on coffee consumption in the computer science department. Anytime in the morning would be fine. Partner: John Day: Tuesday Location: Mattin Ctr Topic: Coffee Time of Day: Morning

  10. Can we learn them? Partner: John Day: Tuesday Location: Mattin Ctr Topic: Coffee Time of Day: Morning I’d like to meet John on Tuesday in the Mattin Center coffee shop. We should discuss policies on coffee consumption in the computer science department. Anytime in the morning would be fine.

  11. Can we learn them? I’d like to meet <partner> on <day> in the <location>. We should discuss policies on <topic>. Anytime in <time of day> would be fine. Partner: John Day: Tuesday Location: Mattin Ctr Topic: Coffee Time of Day: Morning

  12. Generalization I’d like to meet <partner> on <day> in the <location>. We should discuss policies on <topic>. Anytime in <time of day> would be fine. meet <partner> on <day> in <location> . discuss policies on <topic> anytime in <time of day> How could we learn this?

  13. How about if we have templates without text? Person : Mozart Birthyear : 1756 Can we gather text somehow?

  14. Web Search to generate patterns Web pages w/“Mozart” “1756” Sentences with “Mozart” “1756” Substrings with “Mozart” “1756”

  15. How can we pick good patterns? • Frequent ones may be too general • Infrequent ones not that useful • Want precise, specific ones Use held out templates to evaluate patterns

  16. How about pages but no templates? • Have a set of pages marked as either on topic or off topic • Look for all possible patterns • Estimate which patterns are most likely to occur on a marked up page • Manually screen resulting patterns

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