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Yayın Application of ChatGPT in the tourism domain: potential structures and challenges(IEEE, 2023-12-23) Kılıçlıoğlu, Orkun Mehmet; Özçelik, Şuayb Talha; Yöndem, Meltem TurhanThe tourism industry stands out as a sector where effective customer communication significantly influences sales and customer satisfaction. The recent shift from traditional natural language processing methodologies to state-of-The-Art deep learning and transformer-based models has revolutionized the development of Conversational AI tools. These tools can provide comprehensive information about a company's product portfolio, enhancing customer engagement and decision-making. One potential Conversational AI application can be developed with ChatGPT. In this study, we explore the potential of using ChatGPT, a cutting-edge Conversational AI, in the context of Setur's products and services, focusing on two distinct scenarios: intention recognition and response generation. We incorporate Setur-specific data, including hotel information and annual catalogs. Our research aims to present potential structures and strategies for utilizing Language Model-based systems, particularly ChatGPT, in the tourism domain. We investigate the advantages and disadvantages of three different architectures and evaluate whether a restrictive or more independent model would be suitable for our application. Despite the impressive performance of Large Language Models (LLMs) in generating human-like dialogues, their end-To-end application faces limitations, such as system prompt constraints, fine-Tuning challenges, and model unavailability. Moreover, semantic search fails to deliver satisfactory performance when searching filters that require clear answers. To address these issues, we propose a hybrid approach that employs external interventions, the assignment of different GPT agents according to intent analysis, and traditional methods at specific junctures, which will facilitate the integration of domain knowledge into these systems.Yayın TUR2SQL: A cross-domain Turkish dataset for Text-to-SQL(IEEE, 2023-09-15) Kanburoğlu, Ali Buğra; Tek, Faik BorayThe field of converting natural language into corresponding SQL queries using deep learning techniques has attracted significant attention in recent years. While existing Text-to-SQL datasets primarily focus on English and other languages such as Chinese, there is a lack of resources for the Turkish language. In this study, we introduce the first publicly available cross-domain Turkish Text-to-SQL dataset, named TUR2SQL. This dataset consists of 10,809 pairs of natural language statements and their corresponding SQL queries. We conducted experiments using SQLNet and ChatGPT on the TUR2SQL dataset. The experimental results show that SQLNet has limited performance and ChatGPT has superior performance on the dataset. We believe that TUR2SQL provides a foundation for further exploration and advancements in Turkish language-based Text-to-SQL research.Yayın Leveraging transformer-based language models for enhanced service insight in tourism(IEEE, 2023-12-22) Er, Aleyna; Özçelik, Şuayb Talha; Yöndem, Meltem TurhanCustomer feedback is a valuable resource for enhancing customer experience and identifying areas that require improvement. Utilizing user insights allows a tourism company to identify and address problematic points in its service delivery, provide feedback to partner companies regarding their product offerings, and even reconsider agreements by incorporating these opinions when curating their product portfolio. Setur implemented a systematic approach to collecting customer feedback by distributing "after-stay surveys'' to its customers via email following the completion of the agency services provided. Guest answers to open-ended questions that gather opinions about travel experience are analyzed by four tasks: user intention for answering, the sentiment of the review, subjects touched upon, and whom it concerned. For these tasks, transformer-based natural language processing (NLP) models BERT, DistilBERT, RoBERTa, and Electra are fine-Tuned to classify customer reviews. Based on the test results, it is observed that best practices could be gathered using Bert. In addition, we showed that different insights can be obtained from text comments made for two hotels in Aydin, Turkiye. Some users made complaints using neutral sentences. In some cases, people gave high scores to the numerical rating questions, but their open-ended questions could have a negative meaning.Yayın BOUN-ISIK participation: an unsupervised approach for the named entity normalization and relation extraction of Bacteria Biotopes(Association for Computational Linguistics (ACL), 2019-11-04) Karadeniz, İlknur; Tuna, Ömer Faruk; Özgu, ArzucanThis paper presents our participation at the Bacteria Biotope Task of the BioNLP Shared Task 2019. Our participation includes two systems for the two subtasks of the Bacteria Biotope Task: the normalization of entities (BB-norm) and the identification of the relations between the entities given a biomedical text (BB-rel). For the normalization of entities, we utilized word embeddings and syntactic re-ranking. For the relation extraction task, pre-defined rules are used. Although both approaches are unsupervised, in the sense that they do not need any labeled data, they achieved promising results. Especially, for the BB-norm task, the results have shown that the proposed method performs as good as deep learning based methods, which require labeled data.Yayın Assessing ChatGPT's accuracy in dyslexia inquiry(Institute of Electrical and Electronics Engineers Inc., 2024) Eroğlu, Günet; Harb, Mhd Raja AbouDyslexia poses challenges in accessing reliable information, crucial for affected individuals and their families. Leveraging chatbot technology offers promise in this regard. This study evaluates the OpenAI Assistant's precision in addressing dyslexia-related inquiries. Three hundred questions commonly posed by parents were categorized and presented to the Assistant. Expert evaluation of responses, graded on accuracy and completeness, yielded consistently high scores (median=5). Descriptive questions scored higher (average=4.9568) than yes/no questions (average=4.8957), indicating potential response challenges. Statistical analysis highlighted the significance of question specificity in response quality. Despite occasional difficulties, the Assistant demonstrated adaptability and reliability in providing accurate dyslexia-related information.Yayın Sentiment analysis for hotel reviews in Turkish by using LLMs(Institute of Electrical and Electronics Engineers Inc., 2024) Özdemir, Ata Onur; Giritli, Efe Batur; Can, Yekta SaidThe field of sentiment analysis plays a pivotal role in consumer decision-making and service quality improvement within the hospitality industry. This study explores the application of Large Language Models (LLMs) for sentiment analysis of Turkish hotel reviews, contributing to the understanding of customer feedback and satisfaction. We created a dataset of 5,000 reviews by translating an English corpus into Turkish, which was then utilized to evaluate the performance of a state-of-the-art Turkish language model, TURNA. The study demonstrates that LLMs, particularly TURNA, outperform traditional machine learning algorithms and other advanced models in sentiment classification tasks, achieving an accuracy of 99.4%. This research underscores the potential of LLMs to enhance the accuracy of sentiment analysis, offering valuable insights for the tourism and hospitality sectors. The findings contribute to the ongoing evolution of sentiment analysis methodologies and suggest that LLMs can significantly improve t he understanding a nd processing of customer feedback in Turkish hotel reviews.
