Grammatical Profiling for Semantic Change Detection
| Authors |
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| Publication date | 2021 |
| Host editors |
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| Book title | The 25th Conference on Computational Natural Language Learning |
| Book subtitle | CoNLL 2021 : proceedings of the conference : November 10-11, 2021, online |
| ISBN (electronic) |
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| Event | 25th Conference on Computational Natural Language Learning |
| Pages (from-to) | 423–434 |
| Publisher | Stroudsburg, PA: The Association for Computational Linguistics |
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| Abstract |
Semantics, morphology and syntax are strongly interdependent. However, the majority of computational methods for semantic change detection use distributional word representations which encode mostly semantics. We investigate an alternative method, grammatical profiling, based entirely on changes in the morphosyntactic behaviour of words. We demonstrate that it can be used for semantic change detection and even outperforms some distributional semantic methods. We present an in-depth qualitative and quantitative analysis of the predictions made by our grammatical profiling system, showing that they are plausible and interpretable.
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| Document type | Conference contribution |
| Note | With supplementary video |
| Language | English |
| Published at | https://doi.org/10.18653/v1/2021.conll-1.33 |
| Downloads |
2021.conll-1.33
(Final published version)
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