Summary of Syntactic Language Change in English and German: Metrics, Parsers, and Convergences, by Yanran Chen et al.
Syntactic Language Change in English and German: Metrics, Parsers, and Convergences
by Yanran Chen, Wei Zhao, Anne Breitbarth, Manuel Stoeckel, Alexander Mehler, Steffen Eger
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates diachronic trends in syntactic language change in both English and German parliamentary debates from the past 160 years. The authors use five dependency parsers, including Stanford CoreNLP, to analyze 15 metrics related to dependency distance minimization (DDM) and tree graph properties. Their results show that syntactic language change is sensitive to the parser used, contradicting previous work’s reliance on a single parser. Despite this limitation, the study finds similar trends in both languages, with changes in syntactic measures more frequent at sentence length distribution tails. The authors conclude that their analysis provides the most comprehensive evaluation of syntactic language change using modern NLP technology in recent corpora of English and German. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how languages like English and German have changed over time. It uses special computer programs to analyze old speeches from parliament to see what’s happening with language. The results show that different programs give slightly different answers, but they all agree that the two languages are changing in similar ways. This study helps us understand how languages change by using new technology to look at really old texts. |
Keywords
» Artificial intelligence » Nlp
