Unsupervised Morphological Paradigm Completion
| Autor: | Yihui Peng, Huiming Jin, Katharina Kann, Arya D. McCarthy, Chen Xia, Liwei Cai |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Computer science business.industry Perspective (graphical) 02 engineering and technology computer.software_genre Task (project management) 03 medical and health sciences Tree (data structure) 0302 clinical medicine Inflection 030221 ophthalmology & optometry 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Computation and Language (cs.CL) Natural language processing |
| Zdroj: | ACL |
| DOI: | 10.48550/arxiv.2005.00970 |
| Popis: | We propose the task of unsupervised morphological paradigm completion. Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas. From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators. From a cognitive science perspective, this can shed light on how children acquire morphological knowledge. We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation. We perform an evaluation on 14 typologically diverse languages. Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems. Comment: Accepted by ACL 2020 |
| Databáze: | OpenAIRE |
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