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Improving Geocoding by Incorporating Geographical Hierarchy and Attributes into Transformers Networks- [electronic resource]
Improving Geocoding by Incorporating Geographical Hierarchy and Attributes into Transformers Networks- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101926
- ISBN
- 9798380862455
- DDC
- 020
- 저자명
- Zhang, Zeyu.
- 서명/저자
- Improving Geocoding by Incorporating Geographical Hierarchy and Attributes into Transformers Networks - [electronic resource]
- 발행사항
- [S.l.]: : The University of Arizona., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(108 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
- 주기사항
- Advisor: Bethard, Steven.
- 학위논문주기
- Thesis (Ph.D.)--The University of Arizona, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약With the development of artificial intelligence, machines are empowering all aspects of people's lives. However, there are still many shortcomings in AI. For example, AI robots cannot accurately determine the place names in articles like humans. After all, there are too many places with the same name in the world. Therefore, Geocoding, the task of converting location mentions in text to structured spatial data, has recently seen progress thanks to a variety of new datasets, evaluation metrics, and machine-learning algorithms.In this dissertation, I present empirical studies to explore four research questions: 1. Are classic information retrieval techniques competitive with modern neural approaches for toponym resolution? 2. Can transformer-based reranking improve over a strong candidate retrieval baseline? 3. Which kind of context is most effective for toponym resolution? 4. Is it better to approach toponym resolution as an ontology entry ranking paradigm or a geographic attribute prediction paradigm? Based on these questions, this dissertation contains four research projects, in which we first show that leveraging the better candidate generation, transformer-based reranking, and two-stage resolution can improve toponym resolution performance, and then introduce a new paradigm for toponym resoluton, which achieves a new state-of-the-art.
- 일반주제명
- Information science.
- 일반주제명
- Computer science.
- 기타저자
- The University of Arizona Information
- 기본자료저록
- Dissertations Abstracts International. 85-05B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520240214101926
■006m o d
■007cr#unu||||||||
■020 ▼a9798380862455
■035 ▼a(MiAaPQ)AAI30696156
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a020
■1001 ▼aZhang, Zeyu.
■24510▼aImproving Geocoding by Incorporating Geographical Hierarchy and Attributes into Transformers Networks▼h[electronic resource]
■260 ▼a[S.l.]:▼bThe University of Arizona. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(108 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-05, Section: B.
■500 ▼aAdvisor: Bethard, Steven.
■5021 ▼aThesis (Ph.D.)--The University of Arizona, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aWith the development of artificial intelligence, machines are empowering all aspects of people's lives. However, there are still many shortcomings in AI. For example, AI robots cannot accurately determine the place names in articles like humans. After all, there are too many places with the same name in the world. Therefore, Geocoding, the task of converting location mentions in text to structured spatial data, has recently seen progress thanks to a variety of new datasets, evaluation metrics, and machine-learning algorithms.In this dissertation, I present empirical studies to explore four research questions: 1. Are classic information retrieval techniques competitive with modern neural approaches for toponym resolution? 2. Can transformer-based reranking improve over a strong candidate retrieval baseline? 3. Which kind of context is most effective for toponym resolution? 4. Is it better to approach toponym resolution as an ontology entry ranking paradigm or a geographic attribute prediction paradigm? Based on these questions, this dissertation contains four research projects, in which we first show that leveraging the better candidate generation, transformer-based reranking, and two-stage resolution can improve toponym resolution performance, and then introduce a new paradigm for toponym resoluton, which achieves a new state-of-the-art.
■590 ▼aSchool code: 0009.
■650 4▼aInformation science.
■650 4▼aComputer science.
■653 ▼aGeographical hierarchy
■653 ▼aTransformers networks
■653 ▼aTransformer-based reranking
■653 ▼aTwo-stage resolution
■653 ▼aMachine-learning algorithms
■690 ▼a0723
■690 ▼a0984
■690 ▼a0800
■71020▼aThe University of Arizona▼bInformation.
■7730 ▼tDissertations Abstracts International▼g85-05B.
■773 ▼tDissertation Abstract International
■790 ▼a0009
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16935387▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
