Context Free Frequently Asked Questions Detection Using Machine Learning Techniques
Bibliographic record
Abstract
FAQs are the lists of common questions and answers on particular topics. Today one can find them in almost all web sites on the internet and they can be a great tool to give information to the users. Questions in FAQs are usually identified by the site administrators on the basis of the questions that are asked by their users. While such questions can respond to required information about a service, topic, or particular subject, they can not easily be distinguished from non-FAQ questions. This paper describes machine learning based parsing and question classification for FAQs. We demonstrate that questions for FAQs can be distinguished from other types of questions. Identification of specific features is the key to obtaining an accurate FAQ classifier. We propose a simple yet effective feature set including bag of words, lexical, syntactical, and semantic features. To evaluate our proposed methods, we gathered a large data set of FAQs in three different contexts, which were labeled by humans from real data. We showed that the SVM and Naive Bayes reach the accuracy of 80.3%, which is an outstanding result for the early stage research on FAQ classification. Experimental results show that the proposed approach can be a practical tool for question answering systems. To evaluate the accuracy of our classifier we have conducted an evaluation process and built the questionnaire. Therefore, we compared our classifier ranked questions with user rates and almost 81% similarity of the question ratings gives some confidence.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".