Current Automatic Speech Recognition (ASR) systems mainly take into account acoustic, lexical and local syntactic information. Long term semantic relations are not used. ASR systems significantly decrease performance when the training conditions and the testing conditions differ due to the noise, etc. In this case the acoustic information can be less reliable. To help noisy ASR system, we propose to supplement ASR system with a semantic module. This module re-evaluates the N-best speech recognition hypothesis list and can be seen as a form of adaptation in the context of noise. For the words in the processed sentence that could have been poorly recognized, this module chooses words that correspond better to the semantic context of the sentence. To achieve this, we introduced the notions of a context part and possibility zones that measure the similarity between the semantic context of the document and the corresponding possible hypothesis. The proposed methodology uses two continuous representations of words: word2vec and FastText. We conduct experiments on the publicly available TED conferences dataset (TED-LIUM) mixed with real noise. The proposed method achieves a significant improvement of the word error rate (WER) over the ASR system without semantic information.