TL;DR
This paper introduces a refined user intent taxonomy for web queries, employs weak supervision with Snorkel for annotation, and demonstrates the effectiveness of rule-based intent classification in large-scale datasets.
Contribution
It proposes a more detailed user intent taxonomy, applies weak supervision for large-scale annotation, and shows rule-based methods are competitive for intent classification.
Findings
High consistency in annotator agreement with the new taxonomy
Weak supervision with Snorkel effectively labels large datasets
Rule-based models achieve state-of-the-art performance in intent classification
Abstract
User intent classification is an important task in information retrieval. In this work, we introduce a revised taxonomy of user intent. We take the widely used differentiation between navigational, transactional and informational queries as a starting point, and identify three different sub-classes for the informational queries: instrumental, factual and abstain. The resulting classification of user queries is more fine-grained, reaches a high level of consistency between annotators, and can serve as the basis for an effective automatic classification process. The newly introduced categories help distinguish between types of queries that a retrieval system could act upon, for example by prioritizing different types of results in the ranking.We have used a weak supervision approach based on Snorkel to annotate the ORCAS dataset according to our new user intent taxonomy, utilising…
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