Papers with Wordnet
ZhuJiu-Knowledge: A Fairer Platform for Evaluating Multiple Knowledge Types in Large Language Models (2024.naacl-demo)
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| Challenge: | evaluating the knowledge of large language models (LLMs) is crucial, and rapid advancement in large language modeling has heightened the importance of model evaluations. |
| Approach: | They propose a fairer benchmark for evaluating multiple knowledge types of LLMs by focusing on commonsense knowledge, world knowledge, and language knowledge. |
| Outcome: | The proposed framework evaluates 14 current mainstream LLMs and provides a detailed discussion and analysis of their results. |
ChainNet: Structured Metaphor and Metonymy in WordNet (2024.lrec-main)
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| Challenge: | In a typical lexicon, word senses are encoded as a list, without inter-sense relations. |
| Approach: | They propose a lexical resource which explicitly identifies the senses of a word's senses by expressing how they are derived from one another. |
| Outcome: | The proposed resource expresses how senses in the Open English Wordnet are derived from one another. |
Aligning Wikipedia with WordNet:a Review and Evaluation of Different Techniques (2020.lrec-1)
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| Challenge: | a reliable alignment between WordNet and Wikipedia is a valuable resource for the creation of new wordnets in other languages and for the development of existing wordnet. |
| Approach: | They evaluate methods for aligning Wikipedia articles with WordNet synsets . they use a new gold and silver standard and a method that creates wordnets in other languages . |
| Outcome: | The proposed methods can be used to evaluate the quality of alignments between Wikipedia and WordNet synsets. |
Synthetic Textual Features for the Large-Scale Detection of Basic-level Categories in English and Mandarin (2021.emnlp-main)
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| Challenge: | Basic-level categories are an important psycholinguistic concept introduced by Rosch et al. . an at-scale algorithm for the automatic determination of BLC exists, but it operates without Rosch-style semantic features. |
| Approach: | They propose a method for the detection of BLC at scale that makes use of Rosch-style semantic features. |
| Outcome: | The proposed method outperforms the current SoA in detecting basic-level categories with an accuracy of 75.0% in English and 80.7% in Mandarin. |