Practical Results for General Models of Temporal, Spatial and Semantic Text Processing

Large-scale language models (LLMs) dominate tasks such as natural language processing and computer vision, but using their spatial and temporal predictive power still needs to be improved. The main focus of the task described in this paper is to offer a technical solution that implements the Time Yards Model (TYM), which means putting in evidence time events and their evolution in a novel book or large text in an unsupervised manner. This article has a ground foundation and uses a manually annotated novel to train multiple state-of-the-art machine learning techniques to divide time segments. The aim is to identify specific text chunks based on several features (part-of-speech and part-of-sentence) to branch off the temporal tracks. This manuscript uses TimeML specification language to mark relevant words in time and space to achieve tempo-spatial investigation. Based on the observations, the proposed algorithm extensively analyses various novels presented for comparison, underlining their main strengths and weaknesses. Comprehensive experiments on diversified datasets show that our proposed solution successfully unlocks the potential for spatial and temporal forecasting with reasonable accuracy. Remarkably, our approach has achieved competitive performance compared to relevant papers from this field. Various text-processing methods are described, and different natural language processing frameworks and libraries are compared.