1. A computer-implemented method for generating a context vector for a given topic in a set of topics, the method comprising: identifying a set of topics appearing in sentences in a corpus; for keywords appearing in the sentences in the corpus, determining a topic confidence value indicating a quality of a match between a keyword in a sentence and a topic in the set of topics by: identifying keywords within the sentence based at least in part on parts of speech contained in the sentence; matching the keywords with one or more stored topics to create a tuple for the sentence, the tuple describing topics that are included in the sentence; assigning a topic confidence value for each topic included in the tuple, the topic confidence value indicating a quality of the match between the topic and a keyword in the sentence; and storing the tuple including the topic confidence values for each topic included in the sentence; determining the context vector for the given topic, the context vector representing a frequency of the given topic co-occurring with a plurality of topics from the set of topics, the context vector comprising: a plurality of context confidence values each represent a relationship between the given topic and a topic from the plurality of topics in the set of topics that co-occur within a sentence from the corpus with the given topic; where each context confidence value of the plurality of context confidence values is based upon a combination of a topic confidence value of the given topic and a topic confidence value of the topic from the plurality of topics in the set of topics that co-occurs within the sentence from the corpus with the given topic; wherein determining the context vector for the given topic comprises: normalizing each topic confidence measure in the context vector; sorting the topic confidence measures in the context vector in descending order; and storing the context vector for the given topic in a memory.