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Han, Jiawei Han, Jiawei Zhai, ChengXiang Abdelzaher, Tarek Mei, Qiaozhu Zhang, Chao 2019-02-06T19:36:25Z 2019-02-06T19:36:25Z 2018-12-03 2018-12 As one of the most important data forms, unstructured text data plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to healthcare and scientific research. In many emerging applications, people's information needs from text data are becoming multi-dimensional---they demand useful insights for multiple aspects from the given text corpus. However, turning massive text data into multi-dimensional knowledge remains a challenge that cannot be readily addressed by existing data mining techniques. In this thesis, we propose algorithms that turn unstructured text data into multi-dimensional knowledge with limited supervision. We investigate two core questions: 1. How to identify task-relevant data with declarative queries in multiple dimensions? 2. How to distill knowledge from data in a multi-dimensional space? To address the above questions, we propose an integrated cube construction and exploitation framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multi-dimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models multiple dimensions in the cube space, thereby distilling multi-dimensional knowledge from data to provide insights along multiple dimensions. Together, these two modules constitute an integrated pipeline: leveraging the cube structure, users can perform multi-dimensional, multi-granular data selection with declarative queries; and with cube exploitation algorithms, users can make accurate cross-dimension predictions or extract multi-dimensional patterns for decision making. The proposed framework has two distinctive advantages when turning text data into multi-dimensional knowledge: flexibility and label-efficiency. First, it enables acquiring multi-dimensional knowledge flexibly, as the cube structure allows users to easily identify task-relevant data along multiple dimensions at varied granularities and further distill multi-dimensional knowledge. Second, the algorithms for cube construction and exploitation require little supervision; this makes the framework appealing for many applications where labeled data are expensive to obtain. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms The student, Chao Zhang, accepted the attached license on 2018-12-02 at 13:48. The student, Chao Zhang, submitted this Dissertation for approval on 2018-12-02 at 14:03. This Dissertation was approved for publication on 2018-12-03 at 10:17. DSpace SAF Submission Ingestion Package generated from Vireo submission #13173 on 2019-02-05 at 11:13:29 Made available in DSpace on 2019-02-06T19:36:25Z (GMT). No. of bitstreams: 3 ZHANG-DISSERTATION-2018.pdf: 16001723 bytes, checksum: 33dbcc1eba134a7f9ddb45de5832d8c2 (MD5) LICENSE.txt: 4207 bytes, checksum: 02f937f556cae3ea0735c1ce842d291b (MD5) PROQUEST_LICENSE.txt: 4553 bytes, checksum: 84435e94bc2e715ebadcbe89164b6023 (MD5) Previous issue date: 2018-12-03 application/pdf http://hdl.handle.net/2142/102465 en Copyright 2018 Chao Zhang data mining multi-dimensional analysis less supervision Multi-dimensional mining of unstructured data with limited supervision text text Computer Science Computer Science University of Illinois at Urbana-Champaign Dissertation Ph.D.