{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/quantitative-analysis-of-the-morphological","title":"Quantitative Analysis of the Morphological Complexity of Malayalam Language","arxiv_id":null,"date":"2020-09-01","proceeding":"Text, Speech, and Dialogue 2020 9","authors":["Kavya Manohar","A R jayan","Rajeev Rajan"],"abstract":"This paper presents a quantitative analysis on the morpho- logical complexity of Malayalam language. Malayalam is a Dravidian language spoken in India, predominantly in the state of Kerala with about 38 million native speakers. Malayalam words undergo inflections, derivations and compounding leading to an infinitely extending lexicon. In this work, morphological complexity of Malayalam is quantitatively analyzed on a text corpus containing 8 million words. The analysis is based on the parameters type-token growth rate (TTGR), type-token ratio (TTR) and moving average type-token ratio (MATTR). The val- ues of the parameters obtained in the current study is compared to that of the values of other morphologically complex languages.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-030-58323-1_7","url_pdf":"https://rdcu.be/b6RVa","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"quantitative-analysis-of-the-morphological","repo_url":"https://github.com/kavyamanohar/malayalam-ttr","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"type","task_name":"Vocal Bursts Type Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}