A mathematical model for universal semantics
This work addresses the challenge of universal semantic processing for applications like automated translation and understanding, though it appears incremental by building on existing statistical and Markov-based methods.
The authors tackled the problem of representing word meanings with language-independent numerical fingerprints by analyzing recurring patterns in texts using a Markov semantic model, achieving automated question-answering and cross-lingual text matching across 14 languages from 5 families.
We characterize the meaning of words with language-independent numerical fingerprints, through a mathematical analysis of recurring patterns in texts. Approximating texts by Markov processes on a long-range time scale, we are able to extract topics, discover synonyms, and sketch semantic fields from a particular document of moderate length, without consulting external knowledge-base or thesaurus. Our Markov semantic model allows us to represent each topical concept by a low-dimensional vector, interpretable as algebraic invariants in succinct statistical operations on the document, targeting local environments of individual words. These language-independent semantic representations enable a robot reader to both understand short texts in a given language (automated question-answering) and match medium-length texts across different languages (automated word translation). Our semantic fingerprints quantify local meaning of words in 14 representative languages across 5 major language families, suggesting a universal and cost-effective mechanism by which human languages are processed at the semantic level. Our protocols and source codes are publicly available on https://github.com/yajun-zhou/linguae-naturalis-principia-mathematica