Extracting Semantic Prototypes and Factual Information from a Large Scale Corpus Using Variable Size Window Topic Modelling
Michał Korzycki, Wojciech Korczyński · Annals of Computer Science and Information Systems · 2014
In this paper a model of textual events composed of a mixture of semantic stereotypes and factual information is proposed.A method is introduced that enables distinguishing automatically semantic prototypes of a general nature describing general categories of events from factual elements specific to a given event.Next, this paper presents the results of an experiment of unsupervised topic extraction performed on documents from a large-scale corpus with an additional temporal structure.This experiment was realized as a comparison of the nature of information provided by Latent Dirichlet Allocation and Vector Space modelling based on Log-Entropy weights.The impact of using different time windows of the corpus on the results of topic modelling is presented.Finally, a discussion is suggested on the issue if unsupervised topic modelling may reflect deeper semantic information, such as elements describing a given event or its causes and results, and discern it from pure factual data.