Leveraging Word-Formation Knowledge for C hinese Word Sense Disambiguation (2021)

Abstract: In parataxis languages like Chinese, word meanings are constructed using specific word-formations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods. In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD. We first construct a large-scale Chinese lexical sample WSD dataset with word-formations. Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation. To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable. Experimental results show that our method brings substantial performance improvement over strong baselines.

## introduction word sense disambiguation (wsd) aims to identify the sense of a polysemous word in a specific context, which benefits multiple downstream tasks @xcite . with copious senseannotated data @xcite , neural wsd methods achieve superior performance by leveraging definitional and relational features in knowledge bases (kb) @xcite @xcite . in parataxis languages like chinese, word meanings are highly correlated with word-formations @xcite , which have not been explored in wsd thus far. specifically, word-formations designate how characters interact to construct meanings. as shown in figure 1 , "征文 1 " with the modifier-head formation means solicited paper, where "征" (solicit) modifies "文" (paper); "征文 2 " with the verb-object formation means solicit paper, where "征" (solicit) operates on "文" (paper). on the flip side, word-formations can be inferred from the characters @xcite . for instance, a character combination of adjective-noun is highly probable to have a modifier-head formation. thus, after correct inference, word-formations can help to disambiguate polysemous words by indicating how characters interact in each sense. in this paper, we propose to leverage wordformation knowledge to enhance chinese wsd. we first construct a large-scale formationinformed chinese lexical sample wsd dataset (ficls). then, we propose a model formbert to explicitly incorporate word-formations into sense disambiguation. to enhance generalizability, we design a word-formation predictor module to predict word-formations for unannotated data. experimental results show that our method brings substantial performance improvement on wsd with a high accuracy on formation prediction, which remains consistent in low-resource settings. word-formation knowledge: instead of combining roots and affixes, chinese words are constructed by characters using word-formations @xcite . word-formations have shown to be effective in multiple tasks like learning embeddings for parataxis languages @xcite @xcite . however, these works lack a clear distinction among different word-formations which require manual annotations. ## the ficls dataset the construction of ficls includes two phases: collecting a base dataset and annotating wordformations. each ficls entry consists of (1) a word, (2) a sense definition, (3) a word-formation, and (4) a context sentence. ## conclusion in this paper, we propose to enhance chinese wsd with word-formation knowledge. we first construct a large-scale formation-informed dataset. then, we propose formbert to incorporate the wordformations into bert and design a formation predictor to ease the reliance on annotated data. experimental results validate the effectiveness of leveraging word-formations for chinese wsd.