## 2 Materials and Methods
### Lexical Features  
Domain concept representation in memory involves two main types of lexical knowledge: (1) representations of words' concepts, which are orthographic and phonological when speaking or writing; and (2) concepts' meanings, which are semantic representations within a given context. The following six types of lexical features representing domain concepts at both the orthographic-phonological level and semantic level were included in our regression analysis. * Concept length (CL): A target concept's length is identical to its number of characters. Word length is closely correlated with psycholinguistic properties such as word familiarity, age of acquisition, and concreteness [26; 27; 28]. * Stroke number (SN): Strokes are crucial in identifying Chinese characters, as each character comprises specific stroke patterns. Extensive research has explored character recognition by varying Chinese characters' number of strokes [29; 30; 31]. We speculated that Chinese words or phrases with a larger number of strokes would be more challenging and time intensive to learn than words with a smaller number of strokes. * Frequency of use (FOU): Frequency is a known proxy for familiarity ratings and can be easily estimated using a corpus [32]. An adequate database increases the correlation between words' log-frequencies and familiarity [33]. We presumed that domain concepts' log-frequency and familiarity ratings would be strongly related. * Chinese phonetic alphabet (CPA): The Chinese phonetic alphabet, known as Pinyin, is a system meant to facilitate characters' pronunciation. Chinese characters' ideographic functions far exceeded their phonetics initially. As such, a word's pronunciation is not always evident from its corresponding character pattern. Little empirical evidence has supported a direct correlation between Pinyin and word familiarity [9; 34]. Nevertheless, Chinese characters' phonetic information enhances word recognition [34]. Pinyin helps readers say unfamiliar words correctly as readers extract phonological information for pronunciation [35; 36]. * Part of speech (POS): A word's part-of-speech tag signifies the word's membership in a syntactic category (e.g., noun, verb, adjective). Similar to Pinyin, POS directly influences word learning [37; 38]. For example, nouns are generally characterized by their concreteness, imageability, and meaningfulness; these words are often easier to learn than verbs [39]. * Semantic information (SI): When using a semantic database such as WordNet or a pretrained word embedding model, semantic information possesses predictive power with respect to familiarity ratings [17; 18; 40]. We hypothesized that, the closer the semantic distance between two domain concepts, the more likely the concepts were to have similar familiarity ratings. We developed a feature extraction program using state-of-the-art NLP techniques to derive lexical features from raw domain concepts. The original domain concepts were compound words. We transformed them into a list of Chinese characters and tokenized the concepts into a set of words with part-of-speech tags using the jieba Chinese processing toolkit. Each character's number of strokes per domain concept was obtained from a stroke dictionary. Each word's frequency and Pinyin expression within a given domain concept were then successively identified with the jieba toolkit and the xpinyin package. Domain \begin{table}
\begin{tabular}{|c|c|c|} \hline \multicolumn{2}{|c|}{**Number of Ratings**} & 1832 \\ \hline \multicolumn{2}{|c|}{**Mean**} & 5.238 \\ \hline \multicolumn{2}{|c|}{**Std**} & 0.633 \\ \hline \multicolumn{2}{|c|}{**Min**} & 2.000 \\ \hline \multicolumn{2}{|c|}{**Max**} & 6.500 \\ \hline \multicolumn{2}{|c|}{**25**} & 5.000 \\ \cline{2-3} \multicolumn{2}{|c|}{**Percentile**} & **50** & 5.500 \\ \cline{2-3} \multicolumn{2}{|c|}{**75**} & 5.500 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of domain concepts’ familiarity ratings in the experimental dataset.