Modeling languages from graph networks
This work addresses language modeling for applications in network systems, bioinformatics, internet search, data mining, and computational linguistics, but appears incremental as it applies existing mathematical theories to this domain.
The authors tackled the problem of modeling the probability distribution of letters in randomly generated words within a language, using set partitions, Young tableaux, and graph theoretical methods, but no concrete results or numbers are provided in the abstract.
We model and compute the probability distribution of the letters in random generated words in a language by using the theory of set partitions, Young tableaux and graph theoretical representation methods. This has been of interest for several application areas such as network systems, bioinformatics, internet search, data mining and computacional linguistics.