Authors:
David Adamczyk
1
and
Jan Hůla
1
;
2
Affiliations:
1
Institute for Research and Applications of Fuzzy Modeling, University of Ostrava, Ostrava, 701 03, Czech Republic
;
2
Czech Technical University in Prague, Prague, Czechia, Czech Republic
Keyword(s):
Large Language Models, Embeddings, Word Representations, Sentence Classification, Efficient Pipeline.
Abstract:
In this paper, we propose an efficient approach for tracking a given phenomenon in a corpus using natural language processing (NLP) methods. The topic of tracking phenomena in a corpus is important, especially in the fields of sociology, psychology, and economics, which study human behavior in society. Unlike existing approaches that rely on universal large language models (LLMs), which are computationally expensive, we focus on using computationally less expensive methods. These methods allow for high data processing speed while maintaining high accuracy. Our approach is inspired by the cascade approach to optimization, where we first roughly filter out unwanted information and then gradually use more accurate models, which are computationally more expensive. In this way, we are able to process large amounts of data with high accuracy using different models, while also reducing the overall cost of computations. To demonstrate the proposed method, we chose a task that consists of fin
ding the frequency of occurrence of a certain phenomenon in a large text corpus, which is divided into individual months of the year. In practice, this means that we can, for example, use Internet discussions to find out how much people are discussing a particular topic. The entire solution is presented as a pipeline, which consists of individual phases that successively process text data using methods selected to minimize the overall cost of processing all data.
(More)