The statistical trade-off between word order and word structure – Large-scale evidence for the principle of least effort

• Published in: PLOS ONE
• Main Authors: Koplenig, Alexander, Meyer, Peter, Wolfer, Sascha, Müller-Spitzer, Carolin
• Format: Text
• Language: English
• Published: Public Library of Science 2017
• Subjects: Research Article; Inupiatun
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5345836/; http://www.ncbi.nlm.nih.gov/pubmed/28282435; https://doi.org/10.1371/journal.pone.0173614

Languages employ different strategies to transmit structural and grammatical information. While, for example, grammatical dependency relationships in sentences are mainly conveyed by the ordering of the words for languages like Mandarin Chinese, or Vietnamese, the word ordering is much less restricted for languages such as Inupiatun or Quechua, as these languages (also) use the internal structure of words (e.g. inflectional morphology) to mark grammatical relationships in a sentence. Based on a quantitative analysis of more than 1,500 unique translations of different books of the Bible in almost 1,200 different languages that are spoken as a native language by approximately 6 billion people (more than 80% of the world population), we present large-scale evidence for a statistical trade-off between the amount of information conveyed by the ordering of words and the amount of information conveyed by internal word structure: languages that rely more strongly on word order information tend to rely less on word structure information and vice versa. Or put differently, if less information is carried within the word, more information has to be spread among words in order to communicate successfully. In addition, we find that–despite differences in the way information is expressed–there is also evidence for a trade-off between different books of the biblical canon that recurs with little variation across languages: the more informative the word order of the book, the less informative its word structure and vice versa. We argue that this might suggest that, on the one hand, languages encode information in very different (but efficient) ways. On the other hand, content-related and stylistic features are statistically encoded in very similar ways.