Lexical bundles in computational linguistics academic literature

Corpus linguistics has been in the spotlight for the last decade, with the usage of modern computers and technologies deeper understanding of languages can be obtained. Corpus linguistics helps language teaching for acquiring a better view for language. Language teachers will know what sequence of words and patters tend to co-occur. Unlike previous enormous lists of words which students were forced to memorize them, and were seldom used and typically were forgotten in a short period of time lexical bundles can help language teachers to teach more effectively, and learners can be more fluent in the second language. The first studies in lexical bundles include Firth 1964 (Firth, J. R. (1964).) Biber (2004), (Cortes, V. (2004). Native speakers use a formulaic pattern of speech which they are unaware of but learners from other languages use bundles that are affected (transferred) bye their mother tongue and this solely can be problematic and easily recognizable. moreover in academia, when speakers of source language trying to write, publish, and produce academic literature in the target language their lack the fluency and native like features of a native speaker of that language. Lexical bundles (or as called N-grams) are crucial in getting a fluent academic text. In this paper lexical bundles of 1 to 5 tokens from an 8 million word corpus of academic literature from the Computational Linguistics field and its sub topics such as: Speech recognition, Natural Language Processing, Machine Learning, and Information Retrieval have been extracted and analyzed. On the top of that most of typical criteria for exclusion has been applied to the list as well as calculating MI factor for each result to confirm the results and reaching the target bundles for Computational Linguistics.