Bridging the Gap Between B abel N et and H ow N et: Unsupervised Sense Alignment and Sememe Prediction (2023)

Abstract: As the minimum semantic units of natural languages, sememes can provide precise representations of concepts. Despite the widespread utilization of lexical resources for semantic tasks, use of sememes is limited by a lack of available sememe knowledge bases. Recent efforts have been made to connect BabelNet with HowNet by automating sememe prediction. However, these methods depend on large manually annotated datasets. We propose to use sense alignment via a novel unsupervised and explainable method. Our method consists of four stages, each relaxing predefined constraints until a complete alignment of BabelNet synsets to HowNet senses is achieved. Experimental results demonstrate the superiority of our unsupervised method over previous supervised ones by an improvement of 12% overall F1 score, setting a new state of the art. Our work is grounded in an interpretable propagation of sememe information between lexical resources, and may benefit downstream applications which can incorporate sememe information.

## introduction sememes are the minimum semantic units of human languages @xcite . the theory of semantic primitives @xcite hypothesizes that the meaning of a word in any language can be decomposed into a finite set of languageindependent sememes. for example, the english noun plant has distinct senses that correspond to the "factory" and "vegetation" concepts, respectively. the former can be represented by the sememes "industrial", "produce", and "institute/place", and the latter by "crop", "tree", and "flower/grass". (see figure 2 for additional examples.) although sememes provide a way of representing concepts, their incorporation into natural language processing (nlp) has been limited by a lack of available sememe resources for commonly used sense inventories. tremendous efforts have been made to construct sememe knowledge bases (kbs) manually. one of the most widely used is hownet @xcite , which unfortunately is limited to only two languages: english and chinese. a related problem is linking lexical resources to one another. as manually creating semantic knowledge bases remains a challenging and costly process, babelnet @xcite was instead created by combining wordnet @xcite , wikipedia, and other resources. while ba-belnet covers hundreds of languages, it does not include sememe information. previous work on automatically predicting sememes for babelnet concepts has depended on large human labeled data @xcite . various systems for automatically aligning word senses across heterogeneous resources have been proposed to mitigate this issue @xcite @xcite , but those methods do not leverage the unique structure of hownet, which differs from other lexical databases. in this work, rather than attempting to predict sememes directly, as in prior work, we instead attempt to align babelnet concepts and hownet senses. since each hownet sense is annotated with sememes (c.f., figure 2 ), correctly mapping a ba-belnet concept to a hownet sense will effectively add sememe information to the corresponding ba-belnet synset, as well as associate the hownet sense with information unique to babelnet, such as synonymy information. we propose a fully unsupervised algorithm which achieves reliable and stable results, and produces fully explainable mappings. the ability to identify precisely why a particular sememe was associated with a given concept is a unique strength of our method, which we believe will facilitate analysis and development of downstream applications of our work. the results of our experimental evaluation provide evidence that our unsupervised approach substantially outperforms previous supervised methods on the babelnet sememe prediction task by up to 12% f-score. this also indirectly demonstrates the high accuracy of the generated alignment between babelnet and hownet, which we make available to facilitate further research. 1 our contributions are as follows: in this section, we first describe the two multilingual knowledge bases, and then discuss the tasks of sense alignment and sememe prediction. ## methods in this section, we introduce our method for the task of alignment between babelnet and hownet. our method has four stages, each relaxing some constraint until an alignment is found. such an alignment implicitly associates a babelnet synset with a set of sememes from the aligned hownet senses. we describe each of the four stages in a separate subsection. formally, our task is as follows: let b be a babelnet synset. let b @xmath0, . . . , z n } be the set of chinese words in b, and let b @xmath1, . . . , e m } be the set of english words in b. we assume that at least one of b z and b e is non-empty. the output is a set h of one or more hownet senses that express the same concept as b. ## experiments in this section, we describe the experimental evaluation of our sense alignment algorithm. since we are the first to propose an algorithm for aligning babelnet and hownet, there is no gold-annotated dataset for this task that we can use as a test set. to the best of our knowledge, the only dataset that connects babelnet and hownet is the babelsememe dataset, which was constructed for the evaluation of sememe prediction models. therefore, we evaluate our sense-alignment algorithm extrinsically on the task of sememe prediction. in the sememe prediction task, given a babelnet synset b, and a ground set s * of sememes, we are required to identify a set of sememes s ⊆ s * which describe the meaning of b. following prior work, we take the set of all sememes which appear in hownet as s * . the output s is then compared to a gold-standard set of sememes for b in the ## conclusion we have presented a novel unsupervised method for aligning two lexical-semantic knowledge bases, babelnet and hownet. the results of our experiments on leveraging the sense alignment for the task of sememe prediction demonstrate that our algorithm is highly effective, yielding substantially better results than state-of-the-art supervised systems designed specifically for this task. in the future, we would like to leverage sense alignment for other semantic tasks, including word sense disambiguation.