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Question Answering Based on Semantic Graphs

Question Answering Based on Semantic Graphs. Lorand Dali – lorand.dali@ijs.si Delia Rusu – delia.rusu@ijs.si Bla ž Fortuna – blaz.fortuna@ijs.si Dunja Mladeni ć – dunja.mladenic@ijs.si Marko Grobelnik – marko.grobelnik@ijs.si. Overview. Motivation System Overview Question Answering

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Question Answering Based on Semantic Graphs

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  1. Question Answering Based on Semantic Graphs Lorand Dali – lorand.dali@ijs.si Delia Rusu – delia.rusu@ijs.si Blaž Fortuna – blaz.fortuna@ijs.si Dunja Mladenić – dunja.mladenic@ijs.si Marko Grobelnik – marko.grobelnik@ijs.si

  2. Overview • Motivation • System Overview • Question Answering • Document Overview • Facts • Semantic Graph • Document Summary • Conclusions

  3. Motivation

  4. Motivation

  5. Triplets • Facts stated in the text • The core of the sentence (subject, verb, object)

  6. System Overview

  7. Question Answering • Extract facts (triplets) from text • Index triplets to enable structured search on them • Analyze questions to obtain the queries for the triplet search • Retrieve the answer and the document containing it • Browse the document overview

  8. Question Answering

  9. Question Answering • Question types: • Yes/No questions (Do animals eat fruit?), • list questions (What do animals eat?), • reason questions (Why do animals eat fruit?), • quantity questions (How much fruit do animals eat?), • location questions (Where do animals eat?) and • time questions (When do animals eat?).

  10. Document Overview • Analyze the document containing the answer: • Highlight facts described by subject – verb – object triplets (identified in the Penn Treebank parse tree) • Obtain the document semantic graph • View the automatic document summary

  11. Semantic Graph According to traditional Chinese medical belief, mental problems, laziness, malaria, epilepsy, toothache and lack of sexual appetite can be treated with tiger parts, leading to rampant poaching of the animal in Asia , the World Wide Fund ( WWF ) said. Document Plain text format Named entity extraction Co-reference resolution Asia World Wide Fund WWF S – V – O triplet extraction Asia - location World Wide Fund - organization Triplet enhancement Co-reference Semantic Graph WWF -organization

  12. Document Summary Linear SVM Feature Extractor • Features: • linguistic • document • graph Linear Model The Kerinci conservation project, an area of around three million hectares (7. 4 million acres) in west Sumatra, was being supported by funds from the World Bank, Subijanto said. [10.0912] Subijanto, a spokesman for the Forestry Ministry, said Indonesia was commited to protecting the tigers, which live within Sumatra's four designated conservation areas. [9.4155] Ranking

  13. Document Summary There are people wanting tiger products who didn't want them before, " Ron Lilley,coordinator for species conservation at the WWF in Jakarta, told Reuters. Subijanto, a spokesman for the Forestry Ministry, said Indonesia was commited to protecting the tigers, which live within Sumatra's four designated conservation areas. The Kerinci conservation project, an area of around three million hectares (7. 4 million acres) in west Sumatra, was being supported by funds from the World Bank, Subijanto said.

  14. Conclusions • Enhanced question answering system • Question answering, where the answer is supported by documents • Document browsing • Facts • Document semantic graph • Automatic document summary

  15. Conclusions • Future work • System extensions: triplet extraction, named entity recognition • Expand the search to look for answers in ontologies • Relax the requirement that the questions have a predefined form • Improve the document overview functionality by integrating external resources

  16. Thank you! Questions are guaranteed in life, answers aren’t.

  17. Document Summary • Extracted features:

  18. Document Summary Rank (Information Gain)

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