Zuo Zhuan A ncient C hinese Dataset for Word Sense Disambiguation (2022)

Abstract: Word Sense Disambiguation (WSD) is a core task in Natural Language Processing (NLP). Ancient Chinese has rarely been used in WSD tasks, however, as no public dataset for ancient Chinese WSD tasks exists. Creation of an ancient Chinese dataset is considered a significant challenge because determining the most appropriate sense in a context is difficult and time-consuming owing to the different usages in ancient and modern Chinese. Actually, no public dataset for ancient Chinese WSD tasks exists. To solve the problem of ancient Chinese WSD, we annotate part of Pre-Qin (221 BC) text Zuo Zhuan using a copyright-free dictionary to create a public sense-tagged dataset. Then, we apply a simple Nearest Neighbors (k-NN) method using a pre-trained language model to the dataset. Our code and dataset will be available on GitHub.

## introduction word sense disambiguation (wsd) is a crucial aspect of nlp, which identifies the sense of polysemous words that best fit the current context. compared to some languages such as english, a character in chinese, especially in ancient chinese, usually has multiple and varying meanings, which greatly increases the difficulty of word sense disambiguation. at the present time, @xcite ; @xcite ; @xcite ; @xcite have made certain advances on modern chinese wsd tasks. nevertheless, unlike modern chinese, ancient chinese has hardly been explored in wsd tasks for lack of a dataset thus far. the main reason is that the smaller number of chinese characters in the past led to even greater ambiguity in meaning than in modern chinese. there are also fundamental differences in usage between ancient and modern chinese. figure 1 shows a context figure 1: illustration of choosing the right sense from the given context which means: the doctor said that the disease could not be treated. the senses of the target character "为" have different usages in ancient chinese and modern chinese. from the eight and two possible senses of the target character "为" in ancient and modern chinese, the no. 2 sense in ancient chinese, which denotes "treat" best fits the current context. from zuo zhuan, a pre-qin chinese book published late in the 4th century bc. the target character "为" has eight senses in ancient chinese, differing from the two usual senses in modern chinese. without wsd, those unfamiliar with ancient chinese have difficulty determining the correct senses. if wsd can be applied to ancient chinese, it may contribute to the education of ancient chinese and also many other tasks such as machine translation for ancient chinese. previous researchers such as @xcite ; @xcite used few target characters and extracted the contexts to assemble an ancient chinese lexical sample dataset for their wsd tasks. however, no public dataset for ancient chinese wsd has yet been established. consequently, researchers must create their own datasets to test their models for ancient chinese wsd. therefore, we choose to self-produce a public dataset for ancient chinese wsd tasks. in this study, we selected excerpts from zuo zhuan that includes approximately 200,000 char-acters (token). then we annotated the texts with word senses from an open dictionary kangxi to construct our dataset. in addition, we evaluated a supervised k-nn approach using a pre-trained model @xcite for ancient chinese wsd tasks. the main contributions of this paper are as follows: ## related work word sense disambiguation is a task to predict the correct sense using an input word and its context. for example, "bank" has two meanings in english which refers to "a financial institution" and "sloping land". the ambiguity of word can cause noises in downstream tasks. therefore, it is necessary to uniquely determine the meaning of a word. in chinese, especially in ancient chinese, one character usually has multiple and varying meanings. hence, it adds more difficulties in distinguishing different meanings. although there is the aforementioned educational aspect, wsd of ancient chinese can improve machine translation (to modern-chinese) and full-text search systems. ancient chinese. @xcite applied the crf @xcite model to tackle ancient chinese wsd by using contextual words and linguistic features. they tested the model on six target characters with the best average f-score of 83.04% and proved that linguistic features can improve the wsd results for ancient chinese. @xcite built a knowledge repository of ancient chinese polysemous words and proposed an unsupervised method for ancient chinese wsd based on a vector space model. they tested it on ten target characters and obtained an average accuracy of 79.5%. however, both were tested on limited numbers of characters and their datasets were non-public. in our study, we create a public ancient chinese wsd dataset with 25 target characters, and then apply a k-nn approach using a pre-trained language model to our dataset. ## construction of the zuo zhuan ancient chinese wsd dataset since there is no public dataset for ancient chinese wsd, we created the zuo zhuan ancient chinese wsd dataset for ancient chinese wsd. in this section, we describe the process of creating the dataset. ## conclusion and future work in this paper, we created the zuo zhuan ancient chinese wsd dataset, and then evaluated a 1-nn approach using a pre-trained model guwenbert on our dataset. in future, we plan to increase the coverage of our dataset, explore whether this approach can detect unknown senses and improve the performance by adapting the pre-trained model to our dataset. in addition, ancient chinese and modern chinese have changed greatly in word meanings and vocabulary. among these, we would like to make a comparison of the two models for ancient chinese and modern chinese to address following questions: "how well do models optimized for modern language model perform in our dataset?" and "how well does the model for our ancient chinese perform in the modern chinese dataset?"