LEADER 02670nam 22005055 450 001 9910495347903321 005 20251113194130.0 010 $a3-030-78961-6 024 7 $a10.1007/978-3-030-78961-9 035 $a(CKB)5590000000549950 035 $a(MiAaPQ)EBC6711378 035 $a(Au-PeEL)EBL6711378 035 $a(OCoLC)1267476808 035 $a(PPN)257353550 035 $a(DE-He213)978-3-030-78961-9 035 $a(EXLCZ)995590000000549950 100 $a20210823d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTime Expression and Named Entity Recognition /$fby Xiaoshi Zhong, Erik Cambria 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (113 pages) 225 1 $aSocio-Affective Computing,$x2509-5714 ;$v10 311 08$a3-030-78960-8 327 $aChapter 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. 330 $aThis book presents a synthetic analysis about the characteristics of time expressions and named entities, and some proposed methods for leveraging these characteristics to recognize time expressions and named entities from unstructured text. For modeling these two kinds of entities, the authors propose a rule-based method that introduces an abstracted layer between the specific words and the rules, and two learning-based methods that define a new type of tagging scheme based on the constituents of the entities, different from conventional position-based tagging schemes that cause the problem of inconsistent tag assignment. The authors also find that the length-frequency of entities follows a family of power-law distributions. This finding opens a door, complementary to the rank-frequency of words, to understand our communicative system in terms of language use. 410 0$aSocio-Affective Computing,$x2509-5714 ;$v10 606 $aArtificial intelligence 606 $aArtificial Intelligence 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.35 700 $aZhong$b Xiaoshi$01069921 702 $aCambria$b Erik 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910495347903321 996 $aTime expression and named entity recognition$92846426 997 $aUNINA