| 書(shū)目名稱(chēng) | Multidimensional Mining of Massive Text Data | | 編輯 | Chao Zhang,Jiawei Han | | 視頻video | http://file.papertrans.cn/641/640302/640302.mp4 | | 叢書(shū)名稱(chēng) | Synthesis Lectures on Data Mining and Knowledge Discovery | | 圖書(shū)封面 |  | | 描述 | Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific research and healthcare informatics. In many emerging applications, people‘s information need from text data is becoming multidimensional—they demand useful insights along multiple aspects from a text corpus. However, acquiring such multidimensional knowledge from massive text data remains a challenging task.This book presents data mining techniques that turn unstructured text data into multidimensional knowledge. We investigate two core questions. (1) How does one identify task-relevant text data with declarative queries in multiple dimensions? (2) How does one distill knowledge from text data in a multidimensional space? To address the above questions, we develop a text cube framework. First, we develop a cube construction module that organizes unstructured data into a cube structure, by discovering latent multidimensional and multi-granular structure from the unstructured text corpus and allocating documents into the structure. Second, we develop a cube exploitation module that models mul | | 出版日期 | Book 2019 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01914-2 | | isbn_softcover | 978-3-031-00786-6 | | isbn_ebook | 978-3-031-01914-2Series ISSN 2151-0067 Series E-ISSN 2151-0075 | | issn_series | 2151-0067 | | copyright | Springer Nature Switzerland AG 2019 |
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