Abstract
Abstract
In Natural Language Processing (NLP) and Information Extraction, Named Entity Recognition (NER) presents a significant challenge. NER, the process of autonomously identifying entities like Person, Location, and Organization from text, is well-researched in languages like English and Chinese. However, there is a notable gap in research for Turkish, especially in domain-specific areas such as sports.
The sports industry has seen a remarkable transformation with the convergence of sports and technology, impacting areas like performance enhancement, fan engagement, and various aspects of sports management. This change is driven by increasing global investments in sports, influencing diverse fields, including finance, marketing, and psychology. There's an untapped potential in extracting qualitative insights from textual data, offering a deeper understanding of the dynamics between athletes, teams, and supporters.
One key area where further exploration is needed is in applying deep learning techniques to Turkish NER, particularly in comparison with traditional methods. Additionally, there's a lack of research on the interpretability and explainability of transformer-based models in this context.
This study introduces domain-specific Turkish NER data sets, particularly annotated data sets relevant to sports. These data sets are used to evaluate the effectiveness of transformer-based models in Turkish NER. A significant aspect of this research is comparing these models and analyzing how different annotation formats impact the results. The effects of named entity distribution on model performance were also investigated through cross-validation techniques.
Another crucial component of this study is the focus on interpretability. By employing interpretability methods, we aim to uncover the rationale and mechanisms behind the model predictions. This aspect is particularly important in understanding how these models function and make decisions, which is a relatively under-explored area in Turkish NER.
This research not only contributes to the field of NLP and Information Extraction but also has implications for enriching sports research and management practices, providing new insights into the interaction between sports and technology.