TY - JOUR AU - Li, Yanpeng AU - Lin, Hongfei AU - Yang, Zhihao PY - 2009 DA - 2009/07/17 TI - Incorporating rich background knowledge for gene named entity classification and recognition JO - BMC Bioinformatics SP - 223 VL - 10 IS - 1 AB - Gene named entity classification and recognition are crucial preliminary steps of text mining in biomedical literature. Machine learning based methods have been used in this area with great success. In most state-of-the-art systems, elaborately designed lexical features, such as words, n-grams, and morphology patterns, have played a central part. However, this type of feature tends to cause extreme sparseness in feature space. As a result, out-of-vocabulary (OOV) terms in the training data are not modeled well due to lack of information. SN - 1471-2105 UR - https://doi.org/10.1186/1471-2105-10-223 DO - 10.1186/1471-2105-10-223 ID - Li2009 ER -