## 2 Literature Review  
### Corpus Analysis  
Corpus analysis is a field of linguistic analysis research that was introduced with the invention of computers in modern times. Texts could be retrieved from internet resources, books, discourse records, video transcripts, etc. and saved on computers. To deal with the large amount of linguistic data, applying NLP by importing mathematical statistical models is already the norm. The Hidden Markov Model (HMM), decision tree, tree bank, and linear regression algorithms are the most commonly adopted for processing NLs and developing corpus software or NLP techniques. [PERSON] (1995) proposed the likelihood ratio method to calculate the interrelations between tokens, which became an important statistical algorithm for the development of corpus software. It enables the calculation of the 'keyness' of tokens in order to generate keyword lists. [PERSON]'s likelihood ratio method is defined as follows:  
**Definition 1** ([PERSON], 1995).: _Let two discrete random variables \(X_{1}\) and \(X_{2}\) have a binomial distribution. \(n_{1}\) and \(n_{2}\) are the numbers of trials, respectively. \(p_{1}\) and \(p_{2}\) are the success probability of a single trial. \(k_{1}\) and \(k_{2}\) are the total number of successes, respectively. Then, the logarithm of the likelihood ratio for the binomial distribution will be constructed as:_  
\[-2\log\lambda=2[\log L(p_{1},k_{1},n_{1})+\log L(p_{2},k_{2},n_{2})-\log L(p,k_ {1},n_{1})-\log L(p,k_{2},n_{2})] \tag{1}\]  
_where_  
\[L(p,k,n)=p^{k}(1-p)^{n-k} \tag{2}\]  
\[p_{1}=\frac{k_{1}}{n_{1}},\;p_{2}=\frac{k_{2}}{n_{2}},\;\text{and}\;p=\frac{k_ {1}+k_{2}}{n_{1}+n_{2}}. \tag{3}\]  
Corpus software, also known as a concondancer, is mostly based on statistic algorithms that reorganize tokens from the big textual data. Functions of corpus software include word frequency computing, keyness calculation, clusters/n-gram creation, text dispersion identification, plot calculation, and the provision of concordance lines; those functions integrate statistic models, providing linguists with optimal resolutions in investigating linguistic evidence. [PERSON] et al. (2013) listed several applications of corpus analysis: (1) _lexicography_, which indicates the big textual data (i.e., corpus or corpora data with over a million words) analysis exams' tokens' frequency, collocations, syntaxes, semantic usages offering lexicographers novel approaches in establishing contemporary and updateable dictionaries or database, (2) _grammar_, which indicates corpus analytical tools that provide linguists with genuine and modern concordance lines for probing detailed and broad views of linguistic evidence such as grammatical patterns and rules and interrelations between lexicons, (3) _stylistics_, which indicates how corpus analysis could provide series linguistic empirical evidences for defining the genres of literature and conducting literary comparison, (4) _translation_, which indicates that corpus analysis can be used for optimizing the results of human and machine translation, (5) _forensic linguistics_, which indicates the ways corpus analysis is broadly used in legal domains for preventing and investigating crime incidents, investigating offensive and defensive debates in courts, and retrieving knowledge from law data or legal documents by collecting and analyzing legal documents, courtroom data, and other related sources, (6) _sociolinguistics_, which indicates that corpus analysis is also adopted for investigating linguistic usages of different variants (i.e., people's social and economic statuses, levels of education, racial variations, regional variations, and so on).  
To sum up this section, many corpus-based approaches, especially analytical software, are developed with the assistance of mathematical statistical models and are designed to aid linguists in interpreting syntax and semantics and in identifying linguistic patterns. Corpus analysis can also be carried out from qualitative and quantitative angles.