Symmetrization of word alignments is the fundamental issue in statistical machine translation (SMT). In this paper, we describe an novel reformulation of Hierarchical Subsentential Alignment (HSSA) method using F-measure. Starting with a soft alignment matrix, we use the F-measure to recursively split ENGL the matrix into two soft alignment submatrices. A direction is chosen as the same time on the basis of Inversion Transduction Grammar (ITG). In other words, our method simplifies the processing of word alignment as recursive segmentation in a bipartite graph, which is simple and easy to implement. It can be considered as an alternative of growdiag- final-and heuristic. We show its application on phrase-based SMT systems combined with the state-of-the-art approaches. In addition, by feeding with word-to-word associations, it also can be a real-time word aligner. Our experiments show that, given a reliable lexicon translation table, this simple method can yield comparable results with state-of-theart approaches.