# A Data Driven Approach for Raw Material Terminology
## 3 Resource Preparation
### Adding New Serbian Terms to General Lexica Dictionaries  
Terminology from digitized dictionaries of raw material terminology in Serbian was checked by SrpMD and the corpus from the mining domain, for possible adding to SrpMD. We will illustrate this procedure by the results obtained from MD. The Serbian part of MD that contains headwords was transformed into a text, which was then analysed by SrpMD. Out of 12,655 different single words found in the text produced from the dictionary, 9758 were recognized by SrpMD. Among the 2897 (23%) that were not recognised, there were some acronyms (e.g., '_pH_', '_RR_', '_LD_', '_TV_'), names (e.g., '_Western_', '_Bets_', '_Reni_'), archaisms (e.g., '_abanje_' instead of '_habanje_' (wear and tear), '_bolcn_' instead of '_zavrtani_' (screw), etc. ), as well as some OCR errors (despite manual check-up). Based on this analysis, a set of candidates for new entries into SrpMD were prepared (e.g., '_degazacija_' (degassing), '_eksplotabilam_' (exploitable), '_sabirnik_' (busbar), etc.). Each candidate was further checked against the mining corpus, and if the result (basically, its frequency) was satisfactory, it was added to the SrpMD. The same procedure was applied to other dictionaries with Serbian entries. While the comprehensive terminological dictionaries (such as MD) contained a lot of simple words that were missing in SrpMD, smaller dictionaries, as expected, included frequently used terms that were mostly already in SrpMD. Thus, for example, in Electropedia 13% of words were not recognized by SrpMD, while in the Serbian part of the English-Serbian dictionary of terminology in the field of waste management 6% of words were not recognized. In all other dictionaries the percentage of unrecognized words was between 3%-5%, but whether they would be included into SrpMD depended on their frequency in the mining corpus. Besides the digitized dictionaries, the Serbian corpus and the Serbian part of the bilingual corpus from the mining domain were yet another source of new raw material domain terms that did not exist in SrpMD. Extraction of simple words was relatively simple, namely, words that were not recognized by SrpMD were scrutinized, and if frequent enough, they became candidates for being added to SrpMD. Besides, less than 4% of words in the monolingual mining corpus were unrecognised by SrpMD, where approximately 1.3% out of these 4% were proper candidates to be added to SrpMD, the remaining unrecognized words being variables from equations (0.7%), acronyms (1%), low frequency (hapax and typos--0.5%), foreign names and words (0.5%). However, when it comes to terms in the form of terminological phrases, their extraction from corpora becomes much more complicated. Automatic extraction of term candidates for Serbian relies on a procedure presented in [30; 34]. Essentially, it is based on detectingwords in corpora that follow one of the 23 specific syntactic patterns, most frequent for noun terms (AN adjective-noun, NNg noun-noun in genitive case, AAN,...). The first step in this task is to recognise and extract Serbian terminological phrases from the corpus using syntactic patterns, and calculate their frequency. Frequency was the main parameter for determining the rank of a terminological phrase as a candidate for processing for SrpMD. However, other measures of association, such as T-Score, Keyness, Log-likelihood, were also used, as described in detail in [30]. The task then proceeds by lemmatization of candidate terminological phrases, disambiguation for terminological phrases where more lemmas can be produced, and ends by production of the final lemma, which enables production of all inflected forms for each terminological phrase. As in the case of single terms, frequency for terminological phrases was also calculated for each single-word component of the phrase, but for its lemma, not for the exact inflected form. Having in mind free word order in terminological phrases we were looking for a measure more loose than exact match.