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. |
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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. |
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| Outcome: | The proposed system can expand a seed set of terms into a more complete set of words belonging to the same semantic class. |
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. |
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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. |
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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. |
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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. |
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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. |
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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. |
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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. |
Set Generation Networks for End-to-End Knowledge Base Population (2021.emnlp-main)
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| Challenge: | Existing knowledge base population systems require a machine translation task to generate multiple facts, but the fact order is not considered. |
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A Gold Standard for Multilingual Automatic Term Extraction from Comparable Corpora: Term Structure and Translation Equivalents (L18-1)
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| Challenge: | Terms are notoriously difficult to identify, both automatically and manually. |
| Approach: | They propose a method to annotate terms manually from a comparable corpus . they show that the gold standard provides a tool for evaluation and a rich source of information . |
| Outcome: | The proposed method provides a tool for evaluation and rich source of information about terms. |