Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
| Challenge: | SetExpander is a corpus-based system for expanding a seed set of terms into a more complete set of words belonging to the same semantic class. |
| Approach: | They propose a corpus-based system for expanding a seed set of terms into a more complete set of words that belong to the same semantic class. |
| Outcome: | The proposed system can expand a seed set of terms into a more complete set of words belonging to the same semantic class. |
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SetExpander: End-to-end Term Set Expansion Based on Multi-Context Term Embeddings (C18-2)
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Jonathan Mamou, Oren Pereg, Moshe Wasserblat, Ido Dagan, Yoav Goldberg, Alon Eirew, Yael Green, Shira Guskin, Peter Izsak, Daniel Korat
| Challenge: | SetExpander is a corpus-based system for expanding a seed set of terms into a more complete set of words belonging to the same semantic class. |
| Approach: | They propose to use a corpus-based system for expanding a seed set of terms into a more complete set of words that belong to the same semantic class. |
| Outcome: | The proposed system can expand a seed set of terms, validate it, re-expand the expanded set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. |
A Two-Stage Masked LM Method for Term Set Expansion (2020.acl-main)
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| Challenge: | Existing methods for Term Set Expansion are either distributional or pattern-based . Term set expansion is a task of expanding a small seed set of example terms into a larger set of terms that belong to the same semantic category. |
| Approach: | They propose a method which uses neural masked language models to expand a small seed set of terms into a larger set of semantic terms. |
| Outcome: | The proposed method outperforms state-of-the-art methods due to the small seed set size . it uses neural masked language models to query large, pre-trained mlms . |
Empower Entity Set Expansion via Language Model Probing (2020.acl-main)
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| Challenge: | Existing methods for expanding seed entities with new entities belong to the same semantic class are difficult to implement and can lead to accumulative errors. |
| Approach: | They propose an iterative set expansion framework that leverages automatically generated class names to address the semantic drift issue. |
| Outcome: | The proposed framework generates high-quality class names and outperforms state-of-the-art methods significantly. |
Distributional Term Set Expansion (L18-1)
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| Challenge: | Iterative term set expansion methods for distributional semantic models are used to label terms belonging to a sought after term set. |
| Approach: | They compare iterative term set expansion methods for distributional semantic models to the Simple Margin method, an active learning approach to classification using Support Vector Machines. |
| Outcome: | The proposed methods outperform centrality and classification based methods for distributional semantic models over five different term sets. |
SynSetExpan: An Iterative Framework for Joint Entity Set Expansion and Synonym Discovery (2020.emnlp-main)
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| Challenge: | Entity set expansion and synonym discovery are two critical NLP tasks that are often performed separately, without exploring their interdependencies. |
| Approach: | They propose a framework that enables two tasks to mutually enhance each other by including popular entities’ infrequent synonyms into the set, which boosts set expansion recall. |
| Outcome: | The proposed framework can be used to enhance two NLP tasks by including popular entities’ infrequent synonyms into the set, which boosts set expansion recall. |
A Unified Taxonomy-Guided Instruction Tuning Framework for Entity Set Expansion and Taxonomy Expansion (2025.findings-acl)
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| Challenge: | Existing studies view entity set expansion, taxonomy expansion, and seed-guided taxonomies as three separate tasks. |
| Approach: | They propose a taxonomy-guided instruction tuning framework to teach a large language model to generate siblings and parents for query entities. |
| Outcome: | The proposed framework outperforms baselines on multiple benchmark datasets. |
Topic Taxonomy Expansion via Hierarchy-Aware Topic Phrase Generation (2022.findings-emnlp)
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| Challenge: | Existing methods for topic taxonomies focus on frequent terms and local topic-subtopic relations, which leads to limited topic term coverage. |
| Approach: | They propose a framework for topic taxonomy expansion that directly generates topic-related terms belonging to new topics. |
| Outcome: | The proposed framework outperforms baseline methods on two real-world text corpora. |
Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text (2022.findings-naacl)
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| Challenge: | Existing benchmarks for entity set expansion (ESE) are limited to well-formed text and well-defined concepts. |
| Approach: | They propose to use user-generated text to assess the generalizability of ESE methods by identifying phenomena such as non-named entities, multifaceted entities and vague concepts. |
| Outcome: | The proposed methods are based on user-generated text to assess their generalizability and performance. |
Bringing Emerging Architectures to Sequence Labeling in NLP (2026.eacl-long)
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| Challenge: | Pretrained Transformer encoders are the dominant approach to sequence labeling . however, few have been applied to sequence labels on flat or simplified tasks . |
| Approach: | They propose to use pretrained Transformer encoders to model relations across words . they find that the architectures adapt well across tagging tasks that vary in complexity . |
| Outcome: | The proposed architectures perform well across tagging tasks across languages and datasets. |
Transforming Term Extraction: Transformer-Based Approaches to Multilingual Term Extraction Across Domains (2021.findings-acl)
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| Challenge: | Automated Term Extraction (ATE) is a challenging task, with few exceptions. |
| Approach: | They propose to use a transformer-based term extraction model to extract terms from sentences . they also propose to employ a language model for token classification and a sequence model to reduce sentences to terms . |
| Outcome: | The proposed models outperform baselines on the ATE challenge TermEval 2020 dataset in English, French, and Dutch. |