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인간의 경험 공유를 위한 태스크 및 컨텍스트 추출 및 표현
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인간의 경험 공유를 위한 태스크 및 컨텍스트 추출 및 표현

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  • 1. KAIST Education for the World, Research for the Future 인간의 경험 공유를 위한태스크 및 컨텍스트 추출 및 표현 2012. 11. 29 류지희 웹사이언스공학 전공 정보검색 및 자연어처리 연구실
  • 2. Why Human Experience Sharing? Necessity of Experiential Problem Solving Knowledge2 © 2012 IR&NLP Lab. All rights reserved.
  • 3. A. Change a Flat Tire When You Are a Woman Alone 1. Loosen lug nuts on tire. 2. Install spare tire.user User Context Info B. Change a Tire like a Real Woman [On U.S. highway] 1. Call AAA. [1 year driving experience] 2. Be placed on “hold”. [Heading to New York] [Female] 3 © 2012 IR&NLP Lab. All rights reserved.
  • 4. Experience Mining Building a Relational Knowledge about Experiences Experiential Knowledge Distillation Web Context-anchored Experiential Experiential Sentences & Knowledge Context Automatic extraction Aggregation & abstraction Event People Place Time Event People Place TimePlay Soccer Yongho, … Expo Park 2011-08-10 (Type) (Type) (Type) (Type)Play Baseball Chulsoo, … Gapchun Park 2009-09-02 (Sport) (student) (Park) (Summer) … …4 © 2012 IR&NLP Lab. All rights reserved.
  • 5. From What? Various types of open contents on the Web! How-to Blog Microblog articles posts posts Human Task Event Context Place Semantics mining mining mining Human Experiential KB5 © 2012 IR&NLP Lab. All rights reserved.
  • 6. Human Task Mining6 © 2012 IR&NLP Lab. All rights reserved.
  • 7. Human Task Model Topic hasTopic GoalhasAction hasNextAction ActionhasObject hasTime hasLocation Object Time Location7 © 2012 IR&NLP Lab. All rights reserved.
  • 8. Human Task Extraction GoalTitle How to Make Omelet Soup Make Omelet Soup Action SequenceStep 1 Place the water or canned chicken broth in a large saucepan. (place, water) (place, broth) Boil the sweet yellow onion for several (boil, onion) minutes. (add, broth)Step 2 Add the powdered chicken broth along (boil, soup) with the canned mushrooms. (add, onion) Boil the soup for a few more minutes, and then add the chopped green onion. (drop, egg)Step 3 Drop the eggs into the simmering broth Ingredients a few minutes before youre ready to water broth serve the omelet soup. soup onion egg 8 © 2012 IR&NLP Lab. All rights reserved.
  • 9. Hybrid Extraction Method Eat fruit every day. Sentences Turn off the car. (eat, fruit) (turn off, car) Retrieve and apply Yes Extract Matched? a rule verb and ingredients No Yes Select the best Syntactic Patterns Prob. > threshold label sequence CRFs Model9 © 2012 IR&NLP Lab. All rights reserved.
  • 10. Next Challenging Issues A large fraction of sentences (more than 40%) in how-to instructions are not imperative sentences. Difficulties arising from variations in writing Case Percentage  Scoping ambiguity Scoping  E.g. Clear or glitter nail polish should go on the nails. 13.9% Ambiguity  Anaphora Anaphora 13.1%  E.g. Make it fun and unique Condition 11.9%  Condition Ellipsis 1.9%  E.g. If your computers are only a few years old Implicit 1.3%  Ellipsis meaning  E.g. So why dont you? Grammatical 1.3%  Implicit meaning mistake  E.g. Studying improves grades. (Study hard!) Etc. 56.6%  Grammatical mistake Case Percentage in all the clauses in  E.g. IM a friend! (Make friend relationship in a instance messenger) 30 sample documents10 © 2012 IR&NLP Lab. All rights reserved.
  • 11. Feature SetsFeature Type Feature Name Feature Values Clause Type main, subordinate Person 1st person, 2nd person, 3rd person Syntactic Auxiliary Verb will, shall, can, may, must, able to, … Features Voice active, passive, n/a Tense past, present, future Polarity negated, non-negatedFeature Type Feature Name Examples Obligation • You have to ask about the car. Permission • You can search for the world weather. Modality Features Explanation • The cost for delivery is already included. Supposition • You will have access to the weather.11 © 2012 IR&NLP Lab. All rights reserved.
  • 12. Result: Actionable Clause Detection Task Used Feature Sets F1(NB) F1(DT) F1(SVM) Syntactic Features Actionable 0.933 0.942 0.948 (micro only) Clause Detection + Modality Features 0.862 0.963 0.966 (micro &macro) NB : Naï Bayes ve DT : Decision Tree SVM : Support Vector Machines12 © 2012 IR&NLP Lab. All rights reserved.
  • 13. Bridge to Semantic Web AcTN knowledge representation YAGO knowledge representation13 © 2012 IR&NLP Lab. All rights reserved.
  • 14. Changing Data Representation Current Form Ultimate Target Form Refined tabular Well-designed data records ontology entries [plain text] [well-formed RDF]14 © 2012 IR&NLP Lab. All rights reserved.
  • 15. Event Context Mining15 © 2012 IR&NLP Lab. All rights reserved.
  • 16. What is an Event? Events are defined as situations that happen  Punctual (example 1-2) or last for a period of time (example 3-4)  States in which something holds true (example 5) Examples Ferdinand Magellan, a Portuguese explorer, first reached the islands in search (1) of spices. A fresh flow of lava, gas and debris erupted there Saturday. (2) 11,024 people were evacuated to 18 disaster relief centers. (3) “We’re expecting a major eruption,” he said in a telephone interview early to (4) day. Israel has been scrambling to buy more masks abroad, after a shortage of sev (5) eral hundred thousand gas masks.16 © 2012 IR&NLP Lab. All rights reserved.
  • 17. Event Expressions Event may be expressed in the following forms Type Example Verb A fresh flow of lava, gas and debris erupted there Saturday. Israel will ask the United States to delay a military strike ag Noun ainst Iraq until the Jewish state is fully prepared for a possib le Iraqi attack. A Philippine volcano, dormant for six centuries, began expl Adjective oding with searing gases, thick ash and deadly debris. “There is no reason why we would not be prepared,” Mord Predicative clause echai told the Yediot Ahronot daily. Prepositional phrase All 75 people on board the Aeroflot Airbus died.17 © 2012 IR&NLP Lab. All rights reserved.
  • 18. Feature Sets Basic Features  Named entity (NE) tags and an indication of whether the target noun is prenominal or not. Lexical Semantic Features (LS)  The set of target nouns’ lemmas and their WordNet hypernyms Dependency-based Features (DF)  Nouns become events if they occur with a certain surrounding context, namely, syntactic dependencies  Dependency-based Features sometimes need to be combined with Lexical Semantic Features18 © 2012 IR&NLP Lab. All rights reserved.
  • 19. Comparing with Previous Work An improvement of about 0.22 (precision) and 0.09 (recall) over the state-of-the-art, respectively. Llorens et al. (2010) Proposed Method 0.727 Precision 0.95 0.483 Recall 0.577 0.584 F1 0.718 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.0019 © 2012 IR&NLP Lab. All rights reserved.
  • 20. Place Semantics Mining20 © 2012 IR&NLP Lab. All rights reserved.
  • 21. Place Semantics AS GPS-enabled mobile devices have come into wide use, Location based services catch popularity But it is hard to provide appropriate context-aware services to users when the system only use user’s location, i.e. GPS(latitude, longitude) Contrary to location, Place is space where people impart a meaning If we know the meaning of the place, Place Semantics, we can serve much better suitable services to users21 © 2012 IR&NLP Lab. All rights reserved.
  • 22. Motivation Scenario Recently, Lena moved to Korea from USA. She doesn’t know Korean culture and geography at all because she didn’t leave outside USA before. Is there similar places with Brooklyn Bowl that I often visited in order to relieve stress? How about Olympic Bowling Alley? No. Thanks! It’s NOT the place I wanted. Brooklyn Bowl is a bowling alley in New York City. People enjoy bowling, have a party, drink beer and hold a music event in Brooklyn Bowl.22 © 2012 IR&NLP Lab. All rights reserved.
  • 23. Place Semantics Mining People leave texts about “why they visit, what they do” when they check-in at Place on Foursquare We can know the perception of places from those texts We apply LDA to extract Place Semantics  A document is composed of texts written in a place. “text” Place23 © 2012 IR&NLP Lab. All rights reserved.
  • 24. Similarity between Two PlacesIs there similar places with Brooklyn Bowl thatI often visited in order to relieve stress? How about XL Night Club? Brooklyn Bowl XL Night Club Have a party & Drink beer 41% 32% Enjoy a music show After work 27% 5% 3% Eat food 26% 7% Watch sports game 11% 18% 30% Others24 © 2012 IR&NLP Lab. All rights reserved.
  • 25. Concluding Remarks25 © 2012 IR&NLP Lab. All rights reserved.
  • 26. Application of Our Results Semantic Annotation  Adds diversity and richness to text processing26 © 2012 IR&NLP Lab. All rights reserved.
  • 27. Thank you!27 © 2012 IR&NLP Lab. All rights reserved.
  • 28. KAIST Education for the World, Research for the Future Jihee Ryu (jiheeryu@kaist.ac.kr) http://jihee.kr Yoonjae Jeong (hybris@kaist.ac.kr) Eunyoung Kim (ey_kim@kaist.ac.kr)Sung-Hyon Myaeng (myaeng@kaist.ac.kr) http://ir.kaist.ac.kr/member/professor/ IR&NLP Lab http://ir.kaist.ac.kr
  • 29. Reference1) Jung, Y., Ryu, J., Kim, K., Myaeng, S.H.: Automatic Construction of a Large-Scale Situation Ontology by Mining How-to Instructions from the Web. Web Semantics: Science, Services and Agents on the World Wide Web (2010)2) Ryu, J., Jung, Y., Kim, K., Myaeng, S.H.: Automatic Extraction of Human Activity Knowledge from Method-Describing Web Articles. 1st Workshop on Automated Knowledge Base Construction (2010)3) Park, K.C., Jeong, Y., Myaeng, S.H.: Detecting Experiences from Weblogs. 48th Annual Meeting of the Association for Computational Linguistics (2010)4) Ryu, J., Jung, Y., Myaeng, S.H.: Actionable Clause Detection from Non-imperative Sentences in How-to Instructions: A Step for Actionable Information Extraction. 15th International Conference on Text, Speech and Dialogue (2012)5) Jeong, Y., Myaeng, S.H.: Using Syntactic Dependencies and WordNet Classes for Noun Event Recognition. Workhop on Detection, Representation, and Exploitation of Events in the Semantic Web in conjunction with the 11th International Semantic Web Conference 2012 (2012)6) Carter, E., Donald, J.: Space and place: theories of identity and location. Lawrence & Wishart Ltd. (1993) 29 © 2012 IR&NLP Lab. All rights reserved.
  • 30. Data Collection: How-to Articles General How-to Articles  1,850,725 articles from eHow & 109,781 articles from wikiHow eHow Category Group # doc wikiHow Category Group # doc Computers & Software, Internet 323,289 Computers, Electronics 18,265 Home Building & Design & Safety 307,277 Family Life, Home, Pets, Relationships 18,220 Culture, Holidays, Hobbies, Weddings 238,143 Hobbies, Holidays, Travel 14,514 Business, Investment, Personal Finance 153,458 Health, Sports 14,161 Arts, Entertainment, Music 149,426 Youth 9,161 Family, Parenting, Pets, Plants 135,909 Personal Care, Style 7,031 Cars, Car Repair 108,386 Education, Communications 6,775 Healthcare, Fitness, Sports 103,758 Finance, Business, Work 6,729 Education, Careers, Employment 103,717 Food, Entertaining 6,099 Electronics 101,403 Arts, Entertainment 5,151 Food, Recipes 63,553 Cars, Vehicles 2,316 Fashion, Beauty 62,406 Philosophy, Religion 1,359 Total (As from December 2011) 1,850,725 Total (As from December 2011) 109,78130 © 2012 IR&NLP Lab. All rights reserved.

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