1. A computer-implemented method, comprising: determining a topic divergence value, the topic divergence value substantially proportional to a ratio of a first topic word distribution in a topic document corpus to a second topic word distribution in a document corpus, wherein the topic document corpus is a corpus of topic documents related to a topic, and the document corpus is a corpus of documents that includes the topic documents and other documents; determining a candidate topic word divergence value for a candidate topic word, the candidate topic word divergence value substantially proportional to a ratio of a first distribution of the candidate topic word in the topic document corpus to a second distribution of the candidate topic word in the document corpus, wherein the candidate topic word is not a topic word in a topic dictionary for the topic; and determining whether the candidate topic word is a new topic word for the topic based on the candidate topic word divergence value and the topic divergence value.
1. A computer-implemented method, comprising: determining a topic divergence value, the topic divergence value substantially proportional to a ratio of a first topic word distribution in a topic document corpus to a second topic word distribution in a document corpus, wherein the topic document corpus is a corpus of topic documents related to a topic, and the document corpus is a corpus of documents that includes the topic documents and other documents; determining a candidate topic word divergence value for a candidate topic word, the candidate topic word divergence value substantially proportional to a ratio of a first distribution of the candidate topic word in the topic document corpus to a second distribution of the candidate topic word in the document corpus, wherein the candidate topic word is not a topic word in a topic dictionary for the topic; and determining whether the candidate topic word is a new topic word for the topic based on the candidate topic word divergence value and the topic divergence value. 8. The method of claim 1 , further comprising: determining first word frequencies for existing words and a candidate word in a training corpus, the candidate word defined by a sequence of constituent words, each constituent word being an existing word in a dictionary, and the training corpus comprising a first subset of the document corpus; determining second word frequencies for the constituent words and the candidate word in a development corpus, the development corpus comprising a second subset of the document corpus; determining a candidate word entropy measure based on the second word frequency of the candidate word and the first word frequencies of the constituent words and the candidate word; determining an existing word entropy measure based on the second word frequencies of the constituent words and the first word frequencies of the constituent words and the candidate word; and determining that the candidate word is a candidate topic word if the candidate word entropy measure exceeds the existing word entropy measure.