Table 2 Equations used in SEAM

C-value (a) [18]

\( \left\{\begin{array}{l}\kern12.5em lo{g}_2\left|a\right|\cdot f(a),\kern2em \left|\alpha\ is\ not\ nested\right.\hfill \\ {}lo{g}_2\left|a\right|\left(f(a)-\frac{1}{P\left({T}_{\alpha}\right)}{\displaystyle \sum_{b\epsilon {T}_{\alpha }}f(b)}\right),\kern1em \left| otherwise\right.\hfill \end{array}\right. \)

where:

\( \alpha \) is the candidate string

f(.) is its frequency of occurrence in the corpus

Τ_a is the set of extracted candidate terms that contain a

P(Τ_a) Is the number of these candidate terms

Termhood (a) \( \log \left(\frac{P\left( vote= yes\right)}{P\left( vote= no\right)}\right) \) [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 (Τ_a)

REPEAT_SUB is subgroup (candidate terms that are contained within a) = P (Α_t)

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 = w_i,j = TF_i,j x IDF_i [43]

\( T{F}_{i,j}=\frac{f_{i,j}}{ma{x}_z{f}_{z,j}} \)

where:

TF_i,j is term frequency for keyword k_i in document d_j

f_i,j is the number of times k_i appears in d_j

max_zf_z,j is the maximum frequency across all keywords k_z in d_j

\( ID{F}_i= log\frac{N}{n_i} \)

where:

IDF_i is the inverse document frequency for keyword k_i

N is the total number of documents in the corpus

n_j is the number of documents that k_i appears in

Cosine similarity [43] \( cosine\left(\overrightarrow{w_c},\overrightarrow{w_s}\right)=\frac{\overrightarrow{w_c}\cdot \overrightarrow{w_s}}{\overrightarrow{w_c}\times \overrightarrow{w_s}} \)

\( =\frac{{\displaystyle {\sum}_{i=1}^K}{w}_{i,c}{w}_{i,s}}{\sqrt{{\displaystyle {\sum}_{i=1}^K}{w}_{i,c}^2}\sqrt{{\displaystyle {\sum}_{i=1}^K}{w}_{i,s}^2}} \)

where

w_i,j is defined above