# A Data Driven Approach for Raw Material Terminology
## 3 Resource Preparation
### Adding Bilingual Terms  
Bilingual lists of terms were considered a valuable resource in our approach, and they were generated from two sources, namely, by retrieval from the bilingual MD and by extraction from the aligned bilingual corpus. Term entries from MD were parsed and only those that were confirmed by the mining corpus (monolingual or bilingual) were selected. As mentioned before, one term entry can comprise more terms (single or multi word) and confirmation for each term was looked for. A total of 10,059 term entries from MD were retrieved, with sets of English terms aligned with sets of Serbian terms. The majority of them were subsequently marked by domain (24 different), subdomain (15) and semantic markers (35) as mentioned in Section 3.1. All markers used are subsets of markers--data category values in spMD. Bilingual terminology was extracted from the aligned bilingual domain corpus described in Section 3.2 using terminology extractors for Serbian and English, and Bilte [38]), a tool for chunk alignment [39; 40]. The method combines the approach with existing domain terminology lexicons with term extraction tools. For English, FlexiTerm [41] was used with threshold 3 and TermSuite [42] with threshold 4, based on the experience from other domains and the fact that they use different linguistic filtering. A total of 8456 term candidates for English were selected. For Serbian, the same shallow parser was used as in the case of monolingual extraction (Section 3.3), as well as the same calculation of termhood, a frequency-based measure, which qualified 7825 candidates as terms. Monolingual lists of extracted terms were further expanded by terms retrieved from digitized dictionaries yielding 94,539 English terms and 48,096 Serbian terms. Some terms were found in both datasets: extracted from text and retrieved from dictionaries, namely, a total of 2285 English and 308 Serbian terms. The GIZA++ [43] and Moses toolkit [44] for statistical machine translation (SMT) were used for word alignment. Aligned chunks, presented in the so-called phrase table, are obtained as output from Moses, together with their phrase translation scores. After pruning the phrase table with the threshold probability of 0.85, the remaining chunks were lemmatized and further filtered to select those in which both parts of the pair contain a candidate term from the raw material domain. More details about options and the procedure are available in [40]. The output of this phase contained 8202 Serbian-English pairs as term candidates whose English part was confirmed and 3605 where both language parts were confirmed. In the first step, candidates that were found in digitized dictionaries, or were already assessed as terms, were automatically confirmed, but candidate pairs had to be inspected manually, which yielded a list of 2737 term pairs. General terms, such as, _'red' (row), 'komozicija' (composition), 'din' (diar),'minimal' (minimum), 'izvor informacija' (source of the information),... were excluded, as well as those wrongly aligned, such as: _'nafroved' (pipeline oil),'mireza' (telephone network), 'deponija' (deposit), 'oblik poklopa' (shape of the cover),..._ A wider set of terms will be evaluated in the near future. For evaluation of bilingual candidates, besides frequencies for single terms, we have also used a heuristic for evaluating terminological phrases based on the following observations. The last noun in English noun compounds, which represent the majority of English terminological phrases, as a rule, is the head word carrying the basic meaning, while the preceding nouns are narrowing this meaning, that is, behaving like adjectives. The meaning of a noun compound in English thus flows from right to left, but the Serbian translational equivalent cannot be formed analogously, namely, by a sequence of corresponding Serbian nouns.