Assessing Machine Learning Models for Enhancing Intent Detection in Tourism Chatbots
Why this work is in the frame
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Bibliographic record
Abstract
The tourism sector has recently undergone a significant transformation with the integration of chatbots, enabling users to interact with services through natural language. At the heart of these systems lies the Natural Language Understanding (NLU) component, which processes user input through intent classification and entity extraction. A major challenge, however, is selecting the most effective machine learning method to build robust NLU systems tailored to tourism applications. This study evaluates the performance of various machine learning algorithms for intent classification in tourism-focused chatbots. The models under investigation include Support Vector Machine (SVM), LightGBM, XGBoost, and Random Forest. A tourism-specific dataset was developed for this comparative analysis, with evaluation based on metrics such as accuracy and weighted F1-score. The experimental results indicate that XGBoost, LightGBM, and Random Forest achieve the highest training accuracy in intent classification. These outcomes offer valuable insights for developing effective NLU components in tourism chatbots, improving their ability to interpret user queries accurately.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it