Papers with MEN
Rethinking Word Similarity: Semantic Similarity through Classification Confusion (2025.naacl-long)
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| Challenge: | Word similarity measures cannot capture context-dependent, asymmetrical, polysemous nature of semantic similarity. |
| Approach: | They propose a new measure of similarity that reframes semantic similarity in terms of feature-based classification confusion. |
| Outcome: | The proposed model is comparable to cosine similarity in matching human similarity judgments across several datasets and can measure similarity using predetermined features of interest. |
Malaysian English News Decoded: A Linguistic Resource for Named Entity and Relation Extraction (2024.lrec-main)
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| Challenge: | Standard English and Malaysian English exhibit significant differences in morphosyntactic variations . existing datasets are not sufficient to enhance NLP tasks in Malaysian english . |
| Approach: | They propose to use a Malaysian English news article dataset to refine NER models for Malaysian english. |
| Outcome: | The proposed dataset can improve the performance of NER on Malaysian English. |