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This legal term list was obtained by Dr. María José Marín, who is a lecturer at the University of Murcia, by processing the BLaRC, the British Law Report Corpus, with Terminus (Nazar and Cabré, 2012) and TermoStat (Drouin, 2003). Both ATR(Automatic Term Recognition) methods were selected out of a list of ten (Spark Jones,1972; Church and Gale, 1995; Drouin, 2003; Chung, 2003; Kit and Liu, 2008; Scott,2008; Frantzi et al., 1999; Park et al., 2002; Sclano and Velardi, 2007; Nazar and Cabré, 2012) after evaluating the levels of precision achieved by each of them (see Marín, 2014 for greater details on single-word term recognition methods).

The term inventory which resulted from the validation and merging of the output lists obtained is not intended to be representative of this variety of English, but rather of the corpus itself. Nevertheless, after comparing it with a corpus of legal English textbooks of 196,000 tokens, it was found that the term list offered on the FLAX system covered 67% of the running words in the textbook corpus, hence its usefulness and reliability as a reference list for the study and teaching of legal English.

One of the major features of the words on this vocabulary list, which made it particularly hard to obtain applying ATR methods, is that it is full of sub-technical terms, that is, words which are shared by the general and specialised fields. These words are often employed in both contexts without changing their meaning although they can also convey specialised concepts just in the legal field, retaining a general meaning in general English. As D. Mellinkoff states, one of the major characteristics of legal English is the presence of "common words with uncommon meanings" (1963: 11), which certainly adds to the obscure character of this English variety.

REFERENCES

• Chung, T. M. (2003a). “A corpus comparison approach for terminology extraction”. Terminology, 9 (2): 221-246.
• Church, K.W., Gale, W. (1995). “Inverse Document Frequency (IDF): A measure of Deviations from Poisson”. Proceedings of the Third Workshop on Very Large Corpora. Cambridge: Massachusetts Institute of Technology
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• Daille, B. (1996). “Study and implementation of combined techniques for automatic extraction of terminology”. In Klavans, J.L., Resnik, P. (eds.). The Balancing act: Combining symbolic and statistical approaches to language. Cambridge, MA: MIT Press.
• Drouin, P. (2003). “Term extraction using non-technical corpora as a point of leverage”. Terminology, 9 (1): 99-117.
• Frantzi, K.T., Ananiadou, S. (1999). “The c/nc value domain independent method for multi-word term extraction”. Journal of Natural Language Processing, 3 (2):115-127. Jacquemin, C. (2001). Spotting and discovering terms through NLP. Massachusetts: MIT Press.
• Kit, C., Liu, X. (2008). “Measuring mono-word termhood by rank difference via corpus comparison”. Terminology, 14 (2): 204-229.
• Marín, M.J. (2014). "Evaluation of single-word term recognition methods on a legal English Corpus". Corpora, 9 (1): 83-107.
• Mellinkoff, D. (1963). The Language of the Law. Boston: Little, Brown & Co.
• Nazar, R., Cabré, M.T. (2012). “Supervised Learning Algorithms Applied to Terminology Extraction”. In Aguado de Cea, G., Suárez-Figueroa, M.C., García-Castro, R., Montiel-Ponsoda, E. (eds.). Proceedings of the 10th Terminology and Knowledge Engineering Conference (TKE 2012). Ontology Engineering Group, Association for Terminology and Knowledge Transfer. Madrid, Spain. June, 2012.
• Park, Y., Byrd, R.J., Boguraev, B. (2002). “Automatic Glossary Extraction: Beyond Terminology Association”. In Proceedings of COLING “02 19th Conference on Computational Linguistics. Taipei, Taiwan. August, 2002.
• Sclano, F., Velardi, P., (2007). “A Web Application to Learn the Common Terminology of Interest Groups and Research Communities”. Proceedings of the Conference TIA-2007. Sophia Antipolis, France. October, 2007.
• Schmid, H. (1994). “Probabilistic Part-of-Speech Tagging Using Decision Trees”. In proceedings of the International Conference on New Methods in Language Processing, Manchester, UK.
• Sparck Jones, K. (1972). “A statistical interpretation of term specificity and its application in retrieval”. Journal of Documentation, 28: 11-21.