The paper presents a domain-independent method for automatically extracting multi-word terms from machine-readable specialized language (SL) corpora, combining linguistic and statistical information. The method, called *C-value/NC-value*, consists of two parts: *C-value* and *NC-value*. *C-value* enhances the frequency of occurrence measure by focusing on nested terms, while *NC-value* incorporates context information to improve term extraction. The *C-value* approach uses part-of-speech tagging, a linguistic filter, and a stop-list to rank candidate terms based on their termhood. The *NC-value* method extracts term context words (words that tend to appear with terms) and assigns them weights, which are then used to re-rank the *C-value* list. The evaluation of the method shows that *C-value* improves precision and recall compared to the frequency of occurrence measure, especially for nested terms. The *NC-value* method further enhances the performance by incorporating context information, resulting in a more accurate list of candidate terms.The paper presents a domain-independent method for automatically extracting multi-word terms from machine-readable specialized language (SL) corpora, combining linguistic and statistical information. The method, called *C-value/NC-value*, consists of two parts: *C-value* and *NC-value*. *C-value* enhances the frequency of occurrence measure by focusing on nested terms, while *NC-value* incorporates context information to improve term extraction. The *C-value* approach uses part-of-speech tagging, a linguistic filter, and a stop-list to rank candidate terms based on their termhood. The *NC-value* method extracts term context words (words that tend to appear with terms) and assigns them weights, which are then used to re-rank the *C-value* list. The evaluation of the method shows that *C-value* improves precision and recall compared to the frequency of occurrence measure, especially for nested terms. The *NC-value* method further enhances the performance by incorporating context information, resulting in a more accurate list of candidate terms.