Papers with Automatic Term Extraction
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data (2022.lrec-1)
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| Challenge: | Automatic Term Extraction (ATE) is a key component for domain knowledge understanding and can be used for further NLP applications. |
| Approach: | They propose to fine-tune pre-trained BERT models for automatic Term Extraction (ATE) using cross-lingual and cross-domain transfer learning to extract single and multi-word terms. |
| Outcome: | The proposed models can capture cross-domain and cross-lingual terminologically-marked contexts shared by terms, opening a new design-pattern for ATE. |
Crossing Domains without Labels: Distant Supervision for Term Extraction (2025.emnlp-industry)
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| Challenge: | Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. |
| Approach: | They propose a benchmark spanning seven diverse domains to evaluate ATE performance . they propose psuedo-labels and post-hoc heuristics to ensure generalizability . |
| Outcome: | The proposed model outperforms supervised cross-domain encoder models and few-shot learning baselines on the document- and corpus-levels and its GPT-4o teacher on the benchmark. |
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have improved IE, but their potential for ATE has not been explored. |
| Approach: | They propose a retrieval-based prompting strategy that selects demonstrations according to syntactic rather than semantic similarity in a few-shot setting. |
| Outcome: | The proposed method improves performance on three specialized ATE benchmarks. |