Taxonomies are of great value to many knowledge-rich applications.
As the manual taxonomy curation costs enormous human
effects, automatic taxonomy construction is in great demand. However,
most existing automatic taxonomy construction methods can
only build hypernymy taxonomies wherein each edge is limited
to expressing the “is-a” relation. Such a restriction limits their applicability
to more diverse real-world tasks where the parent-child
may carry different relations. In this paper, we aim to construct
a task-guided taxonomy from a domain-specific corpus, and allow
users to input a “seed” taxonomy, serving as the task guidance. We
propose an expansion-based taxonomy construction framework,
namely HiExpan, which automatically generates key term list from
the corpus and iteratively grows the seed taxonomy. Specifically,
HiExpan views all children under each taxonomy node forming a
coherent set and builds the taxonomy by recursively expanding all
these sets. Furthermore, HiExpan incorporates a weakly-supervised
relation extraction module to extract the initial children of a newly expanded
node and adjusts the taxonomy tree by optimizing its
global structure. Our experiments on three real datasets from different
domains demonstrate the effectiveness of HiExpan for building
task-guided taxonomies.
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This content will become publicly available on February 12, 2025
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering. Manual curation of taxonomies is expensive in terms of human effort, making automatic taxonomy construction highly desirable. In this work, we introduce Chain-of-Layer which is an in-context learning framework designed to induct taxonomies from a given set of entities. Chain-of-Layer breaks down the task into selecting relevant candidate entities in each layer and gradually building the taxonomy from top to bottom. To minimize errors, we introduce the Ensemble-based Ranking Filter to reduce the hallucinated content generated at each iteration. Through extensive experiments, we demonstrate that Chain-of-Layer achieves state-of-the-art performance on four real-world benchmarks.
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- Award ID(s):
- 1901059
- PAR ID:
- 10523095
- Publisher / Repository:
- https://arxiv.org/abs/2402.07386
- Date Published:
- Format(s):
- Medium: X
- Institution:
- University of Notre Dame
- Sponsoring Org:
- National Science Foundation
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