| 书目名称 | Time Expression and Named Entity Recognition |
| 编辑 | Xiaoshi Zhong,Erik Cambria |
| 视频video | |
| 概述 | Presents a synthetic analysis about the characteristics of timexes and entities.Reports the latest findings on recognizing timexes and entities from unstructured text.Opens a door to examine whether m |
| 丛书名称 | Socio-Affective Computing |
| 图书封面 |  |
| 描述 | .This book presents a synthetic analysis about the characteristics of time expressions and named entities, and some proposed methods for leveraging these characteristics to recognize time expressions and named entities from unstructured text. For modeling these two kinds of entities, the authors propose a rule-based method that introduces an abstracted layer between the specific words and the rules, and two learning-based methods that define a new type of tagging scheme based on the constituents of the entities, different from conventional position-based tagging schemes that cause the problem of inconsistent tag assignment. The authors also find that the length-frequency of entities follows a family of power-law distributions. This finding opens a door, complementary to the rank-frequency of words, to understand our communicative system in terms of language use.. |
| 出版日期 | Book 2021 |
| 关键词 | Time Expression; Named Entity; Syntactic Token Types; Tagging Schemes; Power-Law Distribution |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-78961-9 |
| isbn_softcover | 978-3-030-78963-3 |
| isbn_ebook | 978-3-030-78961-9Series ISSN 2509-5706 Series E-ISSN 2509-5714 |
| issn_series | 2509-5706 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
发表于 2025-3-21 19:22:31
