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UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities (2403.04247v2)

Published 7 Mar 2024 in cs.CL

Abstract: Entity Set Expansion (ESE) aims to identify new entities belonging to the same semantic class as a given set of seed entities. Traditional methods primarily relied on positive seed entities to represent a target semantic class, which poses challenge for the representation of ultra-fine-grained semantic classes. Ultra-fine-grained semantic classes are defined based on fine-grained semantic classes with more specific attribute constraints. Describing it with positive seed entities alone cause two issues: (i) Ambiguity among ultra-fine-grained semantic classes. (ii) Inability to define "unwanted" semantic. Due to these inherent shortcomings, previous methods struggle to address the ultra-fine-grained ESE (Ultra-ESE). To solve this issue, we first introduce negative seed entities in the inputs, which belong to the same fine-grained semantic class as the positive seed entities but differ in certain attributes. Negative seed entities eliminate the semantic ambiguity by contrast between positive and negative attributes. Meanwhile, it provide a straightforward way to express "unwanted". To assess model performance in Ultra-ESE, we constructed UltraWiki, the first large-scale dataset tailored for Ultra-ESE. UltraWiki encompasses 236 ultra-fine-grained semantic classes, where each query of them is represented with 3-5 positive and negative seed entities. A retrieval-based framework RetExpan and a generation-based framework GenExpan are proposed to comprehensively assess the efficacy of LLMs from two different paradigms in Ultra-ESE. Moreover, we devised three strategies to enhance models' comprehension of ultra-fine-grained entities semantics: contrastive learning, retrieval augmentation, and chain-of-thought reasoning. Extensive experiments confirm the effectiveness of our proposed strategies and also reveal that there remains a large space for improvement in Ultra-ESE.

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Authors (10)
  1. Yangning Li (49 papers)
  2. Qingsong Lv (10 papers)
  3. Tianyu Yu (20 papers)
  4. Yinghui Li (65 papers)
  5. Shulin Huang (12 papers)
  6. Tingwei Lu (5 papers)
  7. Xuming Hu (120 papers)
  8. Hai-Tao Zheng (94 papers)
  9. Hui Wang (371 papers)
  10. Wenhao Jiang (40 papers)
Citations (1)
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