## 2 Related Work  
Our work mainly involves the estimation of OOVs and low-frequency words' embeddings and designing a sentence representation model. In this section, we introduce the related works of these two aspects. Whether it is static word embedding models such as Word2 Vec, Glove, and faktext [12; 13; 21], or dynamic word embedding models such as BERT, ELMO, and GPT [1; 2; 14], they all extract features from a large number of samples to generate word representations. For OOVs that have never appeared and low-frequency words,  
Figure 1: An entry’s construct and the semantics relationship between the word and the explanatory note. A Chinese word usually contains multiple Chinese characters. Although “train” is not a low-frequency word in Chinese, we use it as an example for demonstration. these models are unable to estimate their representation well [17]. Researchers have studied how to improve the estimate of OOVs and low-frequency words' representations. These methods mainly use the surface features of OOVs and their context to predict the meaning. Three types of embeddings (word, context clue, and subword embeddings) were jointly learned to enrich the OOVs' representations [22]. In [17], an OOV embedding prediction model named hierarchical context encoder (HiCE) was proposed to capture the semantics of context as well as morphological features. Recently, a mimicking approach has been found to be a promising solution to the OOV problem. In [23], an iterative mimicking framework that strikes a good balance between word-level and character-level representations of words was proposed to better capture the syntactic and semantic similarities. In [24], a method was proposed to estimate OOVs' embeddings by referring to pre-trained word embeddings for known words with similar surfaces to target OOVs. In [25], the embeddings of OOVs were determined by the spelling and the contexts in which they appear. The above-mentioned word embedding models that use morphology to infer the representations of OOVs are effective for English. However, they are not necessarily effective for Chinese, because many words with similar forms have very different meanings. An explanatory note in an entry is usually a complete sentence. We naturally think of using the sentence representation model to extract the semantics from the explanatory note and treat it as the semantics of the corresponding word. In recent years, many sentence representation models have been proposed and widely used. Facebook AI's fasttext is a sentence representation model based continuous skip-gram model [12; 21], which can estimate both word representations and sentence representations ([[https://github.com/facebookresearch/fastText](https://github.com/facebookresearch/fastText)]([https://github.com/facebookresearch/fastText](https://github.com/facebookresearch/fastText)), accessed on 5 March 2021). In [26], an unsupervised sentence embedding method (sent2 vec) using compositional n-gram features was proposed to produce general-purpose sentence embeddings. Both fasttext and sent2 vec are all BOW models, and we think that the BOW mechanism is just the simple combination of word embeddings. BERT is a landmark dynamic word embedding model. It learns sentence representations by performing two tasks: masked word prediction (MWP) and next sentence prediction (NSP) [1]. The embedding of token [CLS] in the last layer of BERT is considered as the representation of the input sentence. SBERT-WK is a sentence representation model based on BERT. It calculates the importance of words in sentences through subspace analysis and then weights word embeddings to generate sentence representations [27]. In [28], the framework of neural machine translation (LASER) was adopted to jointly learn sentence representations across different languages. BERT uses a bidirectional self-attention encoder (the transformer) to encode sentences, and LASER uses a BiLSTM. In addition, there are more studies on sentence representations [29; 30; 31].