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In many cases keywords from a restricted set of possible keywords have to be assigned to texts. A common way to find the best keywords is to rank terms occurring in the text according to their tf.idf value. This requires a corpus of texts from which document frequencies can be derived. In this paper we show that we
Due to the huge number of research articles in the biomedical domain, it becomes more and more important to develop methods to find relevant articles of our specific research interests. Keyword extraction is a useful method to find important topics from documents and summarize their major information. Unfortunately
be easily extracted, building respective data banks. Keywords are the important terms, sometimes called, index terms that contain some kind of valuable information about the document. Automatic keyword extraction is the task to identify a small set of words, which can be designated as keywords for that document, and
clustering genes is done in two steps: First, keywords corresponding to all genes of interest from a subset of MEDLINE database were extracted automatically using TF-IDF and Z-scores. In the second step, the classic K-means algorithm was used to group genes into clusters of genes based on the keyword features.
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