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Kyoungryol Kim

Meeting Information Extraction from Meeting Announcement in Korean. Kyoungryol Kim. Table of Contents. Introduction Motivation Goal Problem Definition The Proposed Method Problem Modeling / Checklist Overall Architecture Normalization Process. Introduction. Motivation.

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Kyoungryol Kim

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  1. MeetingInformation Extraction from Meeting Announcement in Korean Kyoungryol Kim

  2. Table of Contents • Introduction • Motivation • Goal • Problem Definition • The Proposed Method • Problem Modeling / Checklist • Overall Architecture • Normalization Process

  3. Introduction

  4. Motivation • Everyday we receive a lot of Meeting Announcement • Conference, Seminar, Workshop, Meeting, Appointment… • Meeting announcement accounts for 17% (30,201 out of 183,022) of emails in Enron Email Dataset. • Smartphone era • Many people manage schedule using online-calendar via smartphonee.g. Google Calendar • But, typing by touch screen keyboard make many errors and even it’s difficult. * Enron Email Dataset, August 21, 2009 version, http://www.cs.cmu.edu/~enron/

  5. Goal • Extracting schedule information from meeting announcement,and update them to the calendar, automatically. Meeting Announcement 무더운 날씨가 본격적으로 시작되는 즈음하여 유니브캐스트의 상반기 평가와 하반기 운영을 위한 정기팀장회의를 개최합니다. 날짜 : 7월 19일(토) 오후 2시 장소 : 민들레영토 민들레영토오는길 지도와 같이 명동역 8번 출구로 나오셔서 쭉 상가 끼고 걸어가시면 저기YMCA빌딩 1층에 있습니다. Extract Update

  6. Problem Definition To find Meeting Location, the problem divided into 2 parts : • Finding locations from the text for each type of predefined complexity. • Named entity disambiguation on found locations. 무더운 날씨가 본격적으로 시작되는 즈음하여 유니브캐스트의 상반기 평가와 하반기 운영을 위한 정기팀장회의를 개최합니다. 날짜 : 7월 19일(토) 오후 2시 장소 : 민들레영토 기본 안건 - 제작지원비 지급 지연에 대한 설명 - 기금 조정 운영안 - 가을 워크샵준비위 구성 - 기타(기타 안건으로 상정할 것이 있으면 각 팀장들은 제안해 주시기 바랍니다) 민들레영토 오는길 지도와 같이 명동역8번 츨구로 나오셔서 쭉 상가 끼고 걸어가시면 저기 YMCA빌딩 1층에 있습니다. 참고하세요 무더운 날씨가 본격적으로 시작되는 즈음하여 유니브캐스트의 상반기 평가와 하반기 운영을 위한 정기팀장회의를 개최합니다. 날짜 : 7월 19일(토) 오후 2시 장소 : 민들레영토 기본 안건 - 제작지원비 지급 지연에 대한 설명 - 기금 조정 운영안 - 가을 워크샵준비위 구성 - 기타(기타 안건으로 상정할 것이 있으면 각 팀장들은 제안해 주시기 바랍니다) 민들레영토 오는길 지도와 같이 명동역8번 츨구로 나오셔서 쭉 상가 끼고 걸어가시면 저기 YMCA빌딩 1층에 있습니다. 참고하세요 1. Finding TargetLocations 2. Disambiguation

  7. The Proposed Method

  8. Problem Modeling Meeting Announcement Text Location on the Map 7. How to represent Location? Extract location strings 1. How to extract location string? Extract address information and limit the boundary 2. How to extract address information? 3. What kind of DB can we use? 4. How to manipulate the query? Search the location from the DB Search the location from external resources 5. What kind of external resources can we use? Disambiguation among found locations 6. What are the measures to find desired location?

  9. Problem List (1/2) • How to extract location strings from the given text? • How to extract address information from location strings? • To search the location, what kind of database can we use? • To search the location, how to manipulate the query? • To search the location, what kind of external resources can we use? • What are the measures to find desired locations among candidates? • How to represent the location ?

  10. Problem List (2/2) - Reorganized • How to extract location strings from the given text? • How to extract address information from location strings? • How to check whether address information is included or not? • How to construct database which can limits boundary of address • To search the location, what resources can we use? • Internal database : How to construct internal database? • External resources : What external resources available? • To search the location, how to manipulate the query? • What are the measures to find desired locations among candidates? • How to represent the location ? • To store the location to the DB • To represent the location on the map

  11. Problem Checklist : (6/6) How to represent the location ? • To store the location to the DB • Uses OpenStreetMap representation • Node / Way / Relation • To represent the location on the map • WGS84 (standard) : ( latitude, longitude [, altitude] )

  12. Representation of Meeting Location • Follows basic representations of the Nodein OpenStreetMap to represent location. • Regard the meeting location as Point-of-Interest • Variable attributes (key-value pair)http://wiki.openstreetmap.org/wiki/Map_Features • used_as_meeting_location=true • search_query=user’s query (comma separated) • Meeting location can be imported to OSM server (interoperability) <node id="850918486" lat="37.4936384" lon="127.0137745" user="cyana" uid="74529" visible="true" version="3" changeset="5478335" timestamp="2010-08-13T02:26:19Z"> <tag k="name" v="교대(Gyodae)"/> <tag k="name:en" v="Gyodae"/> <tag k="name:ko_rm" v="Gyodae"/> <tag k="railway" v="station"/> </node> <node id="368738707" lat="37.4990100" lon="127.0275800" user="cyana" uid="74529" visible="true" version="2" changeset="4370541" timestamp="2010-04-09T08:09:50Z"> <tag k="amenity" v="dentist"/> <tag k="name" v="미소드림치과 (Misodeurim Dental Clinic)"/> <tag k="name:en" v="Misodeurim Dental Clinic"/> <tag k="name:ko" v="미소드림치과"/> <tag k="name:ko_rm" v="Misodeurimchigwa"/> <tag k="ncat" v="치과"/> </node>

  13. node changeset node_tag changeset_tag bounds

  14. Example : bounds • Bounds information constructed by using Google Maps API • Closed-world is South Korea area (possibly can be expanded)

  15. Overall Architecture Training System Testing System Input Document PersonalInformation Expand Gazetteer Finding Target Locations Gazetteer Location NER Trained Models (CRFs,SVMs) Corpus Expansion OpenAPI Map Services Train Models Relation-type Classification Document Annotation Adding Document to Corpus Normalization Disambiguation Training Corpus OUTPUT

  16. Normalization Process Normalization Input Query: 프란치스코교육회관2층 Input Document Pre-Processing : Remove HTML-tag/URL/㈜ Replace (),[],{}with space Finding Target Locations Location NER { “query” : { “full” : “프란치스코교육회관2층”, } } OpenAPI Map Services Relation-type Classification Normalization Split the Query into 2 parts : Main Part / Extra-Part Disambiguation PersonalInformation Main : Chunks include Main location information. Extra : Chunks include Floor/room information. Gazetteer OUTPUT Trained Models (CRFs,SVMs) { “query” : { “full” : “프란치스코교육회관2층”, “main” : “프란치스코교육회관”, “extra” : “2층” } }

  17. Normalization Extract Address Information 1. if query doesn’t have Address information: Without boundary limitation, just do search from the databases and APIs No has Address Info? Input Document has Address info? 1) main query 를 space 단위로 chunking 하고 2) 각 chunk 를 iteration 하면서 - chunk가 “-시”, “-시/-구/-군”, “-동/-가/-면/-읍”, “-리” 로 끝나는지, - DB의 시/구/동/리 칼럼의 값으로 시작되는지 확인하여, 찾아진 칼럼과 값을 저장한다. 3) 주소정보가 포함되어 있다면, 뒤에 번지수까지 포함하고 있는지 확인한다. [0-9]+, [0-9]+\-[0-9]+, [0-9]+번지, [0-9]+\-[0-9]+번지 - 번지수까지 포함되어 있으면, 바로 geocoding. - 번지수는 없으면, 해당지역까지의 bounds 를 db에서 가져옴. Yes include House no? No Get Bounds info from Address (SW, NE) { “query” : { “full” : “프란치스코교육회관2층”, “main” : “프란치스코교육회관”, “extra” : “2층” } } Finding Target Locations Bounds DB Yes Location NER OpenAPI Map Services Relation-type Classification Geocoding by Query Normalization Disambiguation PersonalInformation { “query” : { “full” : “서울시 강남구 삼성동 159-1 무역회관 2001호”, “main” : “서울시 강남구 삼성동 159-1 무역회관”, “extra” : “2001호” }, found_locations : [ { “title” : “대한민국 서울특별시 강남구 삼성동 159-1”, “administrative_address” : “대한민국 서울특별시 강남구 삼성동 159-1”, “geometry_location” : { “lat” : 37.5103598, “lng” : 127.0611803 } ] } { “query” : { “full” : “소공동 코리아나 호텔”, “main” : “소공동 코리아나 호텔”, “extra” : “” }, “limited_bound” : { “name” : “대한민국 서울특별시 중구 소공동”, “southwest” : { lat : 37.4346000, lng : 126.7968000}, “northeast” : { lat : 37.6956000, lng : 127.1823000} } } Gazetteer OUTPUT Trained Models (CRFs,SVMs)

  18. Normalization Extract Address Information 2. if the query have address information(with house number): Geocode the address information and return. (Disambiguation finished) No has Address Info? Input Document Yes include House no? No Get Bounds info from Address (SW, NE) { “query” : { “full” : “프란치스코교육회관2층”, “main” : “프란치스코교육회관”, “extra” : “2층” } } Finding Target Locations Bounds DB Yes Location NER OpenAPI Map Services Relation-type Classification Geocoding by Query Normalization Disambiguation PersonalInformation { “query” : { “full” : “서울시 강남구 삼성동 159-1 무역회관 2001호”, “main” : “서울시 강남구 삼성동 159-1 무역회관”, “extra” : “2001호” }, found_locations : [ { “title” : “대한민국 서울특별시 강남구 삼성동 159-1”, “administrative_address” : “대한민국 서울특별시 강남구 삼성동 159-1”, “geometry_location” : { “lat” : 37.5103598, “lng” : 127.0611803 } ] } { “query” : { “full” : “소공동 코리아나 호텔”, “main” : “소공동 코리아나 호텔”, “extra” : “” }, “limited_bound” : { “name” : “대한민국 서울특별시 중구 소공동”, “southwest” : { lat : 37.4346000, lng : 126.7968000}, “northeast” : { lat : 37.6956000, lng : 127.1823000} } } Gazetteer OUTPUT Trained Models (CRFs,SVMs)

  19. Normalization Extract Address Information 3. if the query have address information(no house number): Get bound information and search the location in the bound. No has Address Info? Input Document Yes include House no? No Get Bounds info from Address (SW, NE) { “query” : { “full” : “프란치스코교육회관2층”, “main” : “프란치스코교육회관”, “extra” : “2층” } } Finding Target Locations Bounds DB Yes Location NER OpenAPI Map Services Relation-type Classification Geocoding by Query Normalization Disambiguation PersonalInformation { “query” : { “full” : “서울시 강남구 삼성동 159-1 무역회관 2001호”, “main” : “서울시 강남구 삼성동 159-1 무역회관”, “extra” : “2001호” }, found_locations : [ { “title” : “대한민국 서울특별시 강남구 삼성동 159-1”, “administrative_address” : “대한민국 서울특별시 강남구 삼성동 159-1”, “geometry_location” : { “lat” : 37.5103598, “lng” : 127.0611803 } ] } { “query” : { “full” : “소공동 코리아나 호텔”, “main” : “소공동 코리아나 호텔”, “extra” : “” }, “limited_bound” : { “name” : “대한민국 서울특별시 중구 소공동”, “southwest” : { lat : 37.4346000, lng : 126.7968000}, “northeast” : { lat : 37.6956000, lng : 127.1823000} } } Gazetteer OUTPUT Trained Models (CRFs,SVMs)

  20. { “query” : { “full” : “소공동 코리아나 호텔”, “main” : “소공동 코리아나 호텔”, “extra” : “” }, “limited_bound” : { “name” : “대한민국 서울특별시 중구 소공동”, “southwest” : { lat : 37.4346000, lng : 126.7968000}, “northeast” : { lat : 37.6956000, lng : 127.1823000} } } Normalization Local Search Input Document Find Candidate Locations Geocoding SWRC Meeting Location DB (Priority 2) User Meeting Location DB (Priority 1) Open API (OpenStreetMap, Naver) (Priority 3) SWRC DB User DB Open API WMS Coordinate Conversion KTM -> WGS84 Finding Target Locations Location NER OpenAPI Map Services Relation-type Classification Remove Duplicated Addresses Normalization { “query” : { “full” : “소공동 코리아나 호텔”, “main” : “소공동 코리아나 호텔”, “extra” : “” }, “limited_bound” : { “name” : “대한민국 서울특별시 중구 소공동”, “southwest” : { lat : 37.4346000, lng : 126.7968000}, “northeast” : { lat : 37.6956000, lng : 127.1823000} }, found_locations: [ { “query” : “밀레니엄 힐튼 서울”, “title” : “밀레니엄 힐튼 서울”, “administrative_address” : “대한민국 서울특별시 중구 태평로1가 61-1”, “geometry_location” : { “lat” : 37.5103598, “lng” : 127.0611803 }, { ..... } ] } Disambiguation PersonalInformation Gazetteer OUTPUT Trained Models (CRFs,SVMs)

  21. Disambiguation Title | Query | Address Original Query 동강밀레니엄래프팅밀레니엄 대한민국 강원도 영월군 영월읍거운리547-1 밀레니엄피시방서현점밀레니엄 대한민국 경기도 성남시 분당구 서현동 307 밀레니엄모텔 밀레니엄 대한민국 광주광역시 북구 오룡동1114-1 서울힐튼호텔밀레니엄 힐튼 서울 대한민국 서울특별시 중구 남대문로5가 395 밀레니엄 힐튼 서울 Input Document • Disambiguation • Number of Matched characters query-title, query-original query, query-address • (Can be used ) Semantic Type / Personal Annotation DB / Distance between locationLandmark • Personal Address book/Search history/GPS log Finding Target Locations Location NER OpenAPI Map Services Relation-type Classification Normalization 서울힐튼호텔: 대한민국 서울특별시 중구 남대문로5가 395 (36.3414225, 127.3914705) (Hotel) Disambiguation PersonalInformation Gazetteer OUTPUT Trained Models (CRFs,SVMs)

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