{"word_count": 222, "character_count": 1441}
A text document for classification is basically represented by a set of word count-based features. The simplest one can be the word frequency feature. And the tf-idf (term frequency–inverse document frequency) weight computes each word’s importance in a document from word counts in the document and word existences in all documents.On this basic representation, we can apply a dimension reduction technique such as feature selection to extract a more effective representation. However, the fact remains that the original representation of text documents is based on word counts. This representation is reasonable because the text classification aims to divide documents mostly according to their contextual similarity and dissimilarity. And the features having the most contextual information are words themselves.On the other hand, the notes in our corpus level 2 need not only contextual features but also formal patterns for classification. As we can find in the examples, the basis that decide whether a note is bibliographic reference or not is rather its form than the contained words. But we should also notice that there are several important phrases or words informing that the note has bibliographic information.Because of this specificity of notes, we try to newly generate the features, which can reflect the sequential form of note texts. In the next postings, we describe a number of our strategies for the feature generation.