Many Natural Language Processing (NLP) techniques have been used in Information Retrieval. The results are not encouraging. Simple methods (stopwording, porter-style stemming, etc.) usually yield significant improvements, while higher-level processing (chunking, parsing, word sense disambiguation, etc.) only yield very small improvements or even a decrease in accuracy. At the same time, higher-level methods increase the processing and storage cost dramatically. This makes them hard to use on large collections. We review NLP techniques and come to the conclusion that (a) NLP needs to be optimized for IR in order to be effective and (b) document retrieval is not an ideal application for NLP, at least given the current state-of-the-art in NLP. Other IR-related tasks, e.g., question answering and information extraction, seem to be better suited.