Semi-automatic construction of word-formation networks.
The article presents a semi-automatic method for the construction of word-formation networks focusing particularly on derivation. The proposed approach applies a sequential pattern mining technique to construct useful morphological features in an unsupervised manner. The features take the form of re...
| Publicado en: | Language Resources & Evaluation Vol. 55; no. 1; pp. 3 - 33 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Mar2021
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=149616767&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 149616767 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Mar2021 vid: 55 iid: 1 pid: 237 pub: Springer Nature artinfo: ui: 149616767 10.1007/s10579-019-09484-2 ppf: 3 ppct: 30 formats: fmt: @attributes: type: P size: 857KB tig: atl: Semi-automatic construction of word-formation networks. aug: au: Lango, Mateusz Žabokrtský, Zdeněk Ševčíková, Magda affil: Faculty of Computing, Institute of Computing Science, Poznan University of Technology, Poznan, Poland Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics, Charles University, Prague, Czech Republic su: Sequential pattern mining Active learning Social interaction Word formation (Grammar) sug: subj: Sequential pattern mining Active learning Social interaction Word formation (Grammar) keyword: Derivation Derivational morphology Learning to rank Lexical network Word-formation ab: The article presents a semi-automatic method for the construction of word-formation networks focusing particularly on derivation. The proposed approach applies a sequential pattern mining technique to construct useful morphological features in an unsupervised manner. The features take the form of regular expressions and later they are used to feed a machine-learned ranking model. The network is constructed by applying the learned model to sort the lists of possible base words and selecting the most probable ones. This approach, besides relatively small training set and a lexicon, does not require any additional language resources such as a list of vowel and consonant alternations, part-of-speech tags etc. The proposed approach is evaluated on lexeme sets of four languages, namely Polish, Spanish, Czech, and French. The conducted experiments demonstrate the ability of the proposed method to construct linguistically adequate word-formation networks from small training sets. Furthermore, the performed feasibility study shows that the method can further benefit from the interaction with a human language expert within the active learning framework. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2021. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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