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Word Semantic Similarity for Morphologically Rich Languages
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In this work, we investigate the role of morphology on the performance of semantic similarity for morphologically rich languages, such as German and Greek.The challenge in processing languages with richer morphology than English, lies in reducing estimation error while addressing the semantic distortion introduced by a stemmer or a lemmatiser.For this purpose, we propose a methodology for selective stemming, based on a semantic distortion metric.The proposed algorithm is tested on the task of similarity estimation between words using two types of corpus-based similarity metrics: co-occurrence-based and context-based.The performance on morphologically rich languages is boosted by stemming with the context-based metric, unlike English, where the best results are obtained by the co-occurrence-based metric.A key finding is that the estimation error reduction is different when a word is used as a feature, rather than when it is used as a target word.
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In the field of semantic similarities of texts, artificial intelligence methods, natural language processing methods, data mining, methods of stylometric analysis, methods of extraction and presentation of knowledge and meaning from documents, data and natural languages (graphical methods such as BG and NOK, data models, semantic networks, neural networks, MultiNets, HSF) are being intensively开发的.
“In the field of the semantic similarities of texts (Harispe et al., 2014; Marsi & Krahmer, 2010; Zervanou et al., 2014), artificial intelligence methods are being intensively developed, natural language processing methods, data mining, methods of stylometric analysis of text, methods of extraction and presentation of knowledge and meaning from documents (Jakupović et al., 2013; Rau”
From Taxonomy of academic plagiarism methods (2021) · cites this paper as Zervanou et al., 2014
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- Ntais, G. (2006). Development of a stemmer for the Greek language. Master's thesis, Stockholm Univer- sity -Royal Institute of Technology.
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Metadata
- Authors
- Zervanou, K., Iosif, E., & Potamianos, A.
- Affiliations
- University of Southern California (US)
- Athena Research and Innovation Center In Information Communication & Knowledge Technologies (GR)
- National Technical University of Athens (GR)
- Venue
- Proceedings of the Language Resources and Evaluation Conference
- Published
- 2014-05
- Pages
- 1642-1648
- ISSN
- 2522-2686
- OpenAlex
- W2251103234
- Cited by
- 10
- References
- 15
- Citations by year
- 2025: 1 · 2021: 1 · 2019: 1 · 2018: 2 · 2017: 2 · 2016: 1 · 2015: 2
- FWCI
- 0.98
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Language Resources and Evaluation Conference (LREC) (publishedVersion, cc-by-nc)
- TU/e Research Portal (submittedVersion)
- Language Resources and Evaluation
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › Language and Linguistics › Syntax, Semantics, Linguistic Variation
- Keywords
- word similarity, morphologically rich languages, semantic distortion, German, Greeks, estimation error reduction
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