A ncient C hinese Glyph Identification Powered by Radical Semantics (2024)

Abstract: The ancestor of Chinese character – the ancient characters from about 1300 BC to 200 BC are not fixed in their writing glyphs. At the same or different points in time, one character can possess multiple glyphs that are different in shapes or radicals. Nearly half of ancient glyphs have not been deciphered yet. This paper proposes an innovative task of ancient Chinese glyph identification, which aims at inferring the Chinese character label for the unknown ancient Chinese glyphs which are not in the training set based on the image and radical information. Specifically, we construct a Chinese glyph knowledge graph (CGKG) associating glyphs in different historical periods according to the radical semantics, and propose a multimodal Chinese glyph identification framework (MCGI) fusing the visual, textual, and the graph data. The experiment is designed on a real Chinese glyph dataset spanning over 1000 years, it demonstrates the effectiveness of our method, and reports the potentials of each modality on this task. It provides a preliminary reference for the automatic ancient Chinese character deciphering at the glyph level.

## introduction the ancient chinese characters before 200 bc are largely different from the modern ones, which mainly include the oracle bone script (oracle) in about 1300 bc @xcite , the chinese bronze script (bronze, about 1000 bc) @xcite and the script belonging to the warring states period (states, about 400 bc) @xcite . the glyphs of ancient character are not fixed. figure 1 shows 17 glyphs of the character "春" (spring) distributed in 5 time stages of evolution @xcite . although they look different, there are potential semantic relations between their radicals: their radical systems both express the "spring" meaning by * * corresponding authors portraying a picture of "plants are growing under the sun". thus, many of the glyphs contain semantic radicals of "屮" (grass), "木" (tree) and "日" (sun), and nearly all of glyphs contain the phonetic radical "屯" ("屯" and "春" are homophones). at present, about half of the ancient glyphs have not been deciphered, which means that we do not know the modern chinese characters they corresponded, and can not completely interpret the historical documents containing them. experts have to compare all related glyphs in history for reasoning. however, there are tens of thousands of glyphs distributed in various time stages with complex relationships between their radicals and shapes, which makes deciphering a difficult work that heavily relies on human memory and experience. please imagine a real scenario: given a unknown ancient chinese glyph, we can obtain its images and radical information, and learn the radical semantics and potential usage patterns from the known glyphs in history. then, can we automatically speculate the chinese character to which the unknown glyph belongs? and to what extent the visual and radical information can support this task respectively? answering these questions has significance for ancient chinese character deciphering. in this background, our contributions include: (1) we propose an innovative task of ancient chinese glyph identification, to infer the chinese character label for unknown glyphs in training set based on the image and radical information (figure 2 ); this paper provides a preliminary reference for the ancient character deciphering. it has potential applications in ancient chinese, ancient character, history, and other related fields. for instance, to help experts discover the top-k possible character labels for the unknown glyph, as well as their relevant glyphs in history; and to improve the effects of optical character recognization through radical semantics. the organization of this paper is as follows: section 2 presents the related works; section 3 describes the key knowledge and the task definition; section 4 introduces cgkg; section 5 introduces our glyph identification framework (mcgi); the evaluation is proposed in section 6; section 7 describes the limitations and future works; and section 8 concludes this paper. multimodal methods: the related works are distributed in domains of vision-and-language models @xcite @xcite , nlp and multimodal knowledge graph representation @xcite @xcite . most of the multimodal models follow an universal workflow: generating the embedding for each modality through the pre-trained uni-modal encoders, such as bert @xcite , the deep residual network (resnet) @xcite , and the graph attention network (gat) @xcite , respectively for textual, visual and graph modality. these embeddings will be fed into a cross-modal encoder for data fusion, which can be a simply dot product operation, multimodal attention mechanisms or more complicated transformers. vilt @xcite took shallow embedding layers and used transformer on textual and visual embedding interactions. in the knowledge graph domain, hrgat @xcite proposed a multimodal fusion method based on text and visual co-attention. imf @xcite proposed a two-stage interactive multimodal fusion framework for link prediction. ## chinese glyph knowledge graph as shown in figure 3 , the cgkg has two types of entities of glyph and radical, and two relations: the inclusion relation between glyph and radical rel 1 (g, r), and the semantic relation between two radicals rel 2 (r, r). two radicals would be linked with rel 2 if they met the following relationships: the first five relations were annotated by our experts, and the last one was automatically extracted from dataset. we automatically extracted interchangeable radical pairs from all of variant glyph pairs in dataset: if only the target radical pair are replaced with each other and other parts are the same, they will be set as the candidate, the radical pairs appeared over 2 times will be added into kg. these relations are not independent but highly relevant. we do not specify the relationship categories, if two radicals satisfy any of above professional indicators, we will add an edge between them in kg, and finally construct an undirected graph. ultimately, there are 1907 edges between 657 radical nodes in kg. ## limitations the limitations of this work includes: currently, we are associating chinese glyphs with more textual resources such as the historical documents, dictionary records and so on. the ultimate goal is to provide a complete methodology and refined model for ancient chinese character deciphering. and in application level, we will develop the glyph recognization and recommendation serves based on this work. ## conclusion this paper introduces an innovative research of glyph identification of ancient chinese characters and devises a knowledge powered multimodal method based on visual and radical semantic information for this task. the results proved the effectiveness of our glyph knowledge graph and the multimodal method, which achieves the best results in most indicators, and shows the potentials of visual and radical information in this task for the first time. this work can be applied in the related fields of ancient chinese linguistics and characters.