Research on the parallel text clustering algorithm based on the semantic tree

Since the semantic relationship between words is neglected, the results of the text clustering algorithms that only use word frequency are not precision. In this paper, a semantic tree based text clustering algorithm which is based on WordNet is proposed. In order to reduce the time complexity, we adopt parallel algorithm in multi-processes model. This parallel algorithm starts some processes at the same time. The master process undertakes the task of data partitioning, sending information, collecting information and clustering the result. The slave processes basically are in charge of statistics of word frequency, calculating the weights and getting hypernyms of some words according to the semantic tree. The results of experiment show that this algorithm is not only higher in precision, but also with lower time complexity.