Table 2.

Equations used in SEAM

C-value (a) [18]	$\left\{\begin{array}{l}lo{g}_2\left|a\right|\cdot f\left(a\right),\left|\alpha \mathit{is}\mathit{not}\mathit{nested}\right.\hfill \\ lo{g}_2\left|a\right|\left(f\left(a\right)-\frac{1}{P\left(T_{\alpha }\right)}{\displaystyle \sum _{bϵT_{\alpha }}f\left(b\right)}\right),\left|\mathit{otherwise}\right.\hfill \end{array}\right.$
	where:
	$\alpha$ is the candidate string
	f(.) is its frequency of occurrence in the corpus
	Τa is the set of extracted candidate terms that contain a
	P(Τa) Is the number of these candidate terms
Termhood (a) $log\left(\frac{P\left(\mathit{vote}=\mathit{yes}\right)}{P\left(\mathit{vote}=\mathit{no}\right)}\right)$ [53]	= −0.7836 +
	0.7541* FirstPOS _ ADJECTIVE –
	1.3722* FirstPOS _ ADVERB +
	0.3541* FirstPOS _ NOUN +
	1.4182 * FirstPOS _ VERB –
	0.7722 * LastPOS _ ADJECTIVE +
	2.2576 * LastPOS _ ADVERB +
	0.0285 * LastPOS_NOUN +
	0.6038 * LastPOS _ VERB +
	1.2899 * NP _ VALUE +
	1.0475 * REPEAT _ SUP _ GREATER _ MEDIAN +
	0.8417 * REPEAT _ SUB _ GREATER _ MEDIAN +
	0.8422 * DISTINCT _ PERHOST _ GREATER _ THAN _ MEDIAN
	where:
	POS is Part of Speech tag
	REPEAT_SUP is number of supra (candidate terms containing a) = P (Τa)
	REPEAT_SUB is subgroup (candidate terms that are contained within a) = P (Αt)
	NP_VALUE is a a noun phrase
	DISTINCT_PER_HOST is equivalent to document frequency
	MEDIAN is calculated for the whole document set
TF-IDF = wi,j = TFi,j x IDFi [43]	$T{F}_{i,j}=\frac{f_{i,j}}{ma{x}_z{f}_{z,j}}$
	where:
	TFi,j is term frequency for keyword ki in document dj
	fi,j is the number of times ki appears in dj
	maxzfz,j is the maximum frequency across all keywords kz in dj
	$\mathit{ID}{F}_i=\mathit{log}\frac{N}{n_i}$
	where:
	IDFi is the inverse document frequency for keyword ki
	N is the total number of documents in the corpus
	nj is the number of documents that ki appears in
Cosine similarity [43] $\mathit{cosine}\left(\overrightarrow{w_c},\overrightarrow{w_s}\right)=\frac{\overrightarrow{w_c}\cdot \overrightarrow{w_s}}{\overrightarrow{w_c}\times \overrightarrow{w_s}}$	$=\frac{{\displaystyle {\sum }_{i=1}^K}{w}_{i,c}{w}_{i,s}}{\sqrt{{\displaystyle {\sum }_{i=1}^K}{w}_{i,c}^2}\sqrt{{\displaystyle {\sum }_{i=1}^K}{w}_{i,s}^2}}$
	where
	wi,j is defined above