A Methodology for Hierarchical Classification of Semantic Answer Types of Questions
Bibliographic record
Abstract
Question answering systems have recently been integrated with many smart devices and search engines. Answer type prediction plays an important role in question answering systems as it can help filter irrelevant results and improve overall search and retrieval performance. Here, we present our approach for answer type prediction using the datasets provided for the International Semantic Web Conference (ISWC 2020) SMART Task Challenge. Predicting granular answer types for a question from a big knowledge graph is a greater challenge due to the large number of possible classes. Thus, we propose a 3-step approach to tackle the challenge task. We start with building a classifier that predicts the category of the types and build another classifier just for resource types. The latter model will predict the most general (frequent) type for each question, ignoring type hierarchy. We use a multi-class text classification algorithm built-in fastai library for these two models. The models’ accuracies are 0.95 and 0.73 for category and generic type classification respectively in the validation set (20\% randomly chosen samples) of the DBPedia dataset. Next, we train a third classifier to find more specific types (sub-classes) for each question based on the previous general predicted types. We achieve 0.62 and 0.61 using NDCG@5 and NDCG@10 metrics respectively for the test set.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".