02670nam 22005055 450 991049534790332120251113194130.03-030-78961-610.1007/978-3-030-78961-9(CKB)5590000000549950(MiAaPQ)EBC6711378(Au-PeEL)EBL6711378(OCoLC)1267476808(PPN)257353550(DE-He213)978-3-030-78961-9(EXLCZ)99559000000054995020210823d2021 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierTime Expression and Named Entity Recognition /by Xiaoshi Zhong, Erik Cambria1st ed. 2021.Cham :Springer International Publishing :Imprint: Springer,2021.1 online resource (113 pages)Socio-Affective Computing,2509-5714 ;103-030-78960-8 Chapter 1. Introduction -- Chapter 2. Literature Review -- Chapter 3. Data Analysis -- Chapter 4. SynTime: Token Types and Heuristic Rules -- 5. TOMN: Constituent-based Tagging Scheme -- Chapter 6. UGTO: Uncommon Words and Proper Nouns -- Chapter 7. Conclusion and Future Work.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.Socio-Affective Computing,2509-5714 ;10Artificial intelligenceArtificial IntelligenceArtificial intelligence.Artificial Intelligence.006.35Zhong Xiaoshi1069921Cambria ErikMiAaPQMiAaPQMiAaPQBOOK9910495347903321Time expression and named entity recognition2846426UNINA