Assessing Machine Learning Models for Enhancing Intent Detection in Tourism Chatbots
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".