3 accepted papers
Previous evaluations of large language models (LLMs) focused on the perspective of various tasks or abilities. In this paper, we propose to evaluate from a linguistic viewpoint and argue that morpheme, a potential linguistic feature that captures both word-formation and lexical semantics, is another…
In parataxis languages like Chinese, word meanings are highly correlated with morphological knowledge, which can help to disambiguate word senses. However, in-depth exploration of morphological knowledge in previous word sense disambiguation (WSD) methods is still lacking due to the absence of publi…
Morphemes serve as a strong linguistic feature to capture lexical semantics, with higher coverage than words and more natural than sememes. However, due to the lack of morpheme-informed resources and the expense of manual annotation, morpheme-enhanced methods remain largely unexplored in Computation…