In order to compare work done under natural language processing, the corpora involved in different studies should be standardized/normalized. Entropy, used as language model performance metric, totally depends on signal information. Whereas, when language is considered semantic information should also be considered. Here we propose a metric that exploits Zipf's and Heaps' power laws to respresent semantic information in terms of signal information and estimates the amount of information anticipated from a corpus of given length in words. The proposed metric is tested on 20 different lengths of sub-corpora drawn from major corpus in Turkish (METU). While the entropy changed depending on the length of the corpus, the value of our proposed metric stayed almost constant which supports our claim about normalizing the corpus.