Enhancing Lexical Relation Mining with Structured Sememe Knowledge (2026.acl-long)
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| Challenge: | Existing top-performing methods for Lexical Relation Mining rely on pre-trained language models yet fail to distinguish nuanced lexical relations. |
| Approach: | They propose a framework to leverage structured sememe knowledge to enhance LRC and LE. |
| Outcome: | The proposed method outperforms existing methods on benchmarks and outperformed the LLMs. |
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| Challenge: | Pre-trained language models can effectively mine lexical relations between word pairs . however, graph features and semantic knowledge of pre-tried models are lacking in the task. |
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| Challenge: | Pre-trained language models (PTLMs) are used to predict lexical relations between words. |
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BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models (2023.findings-acl)
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| Challenge: | a simple unsupervised method for predicting graded lexical entailment in English relies on WordNet . despite its simplicity, our method outperforms all previous methods using WordNet as weak supervision. |
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| Challenge: | Existing methods for document-level relation extraction capture non-local interactions but are not able to capture rich non-linguistic interactions. |
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Lexical Relation Mining in Neural Word Embeddings (2020.coling-main)
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