Word co-occurrence measures co-occurring strength between words in texts. Most of the previous measures use a pre-decided context window to define co-occurrence of words. This size is decided from experience, and it is fixed during the whole process of measure. However, this is not ideal because appropriate window size can be different even in two adjacent sentences of a text. This paper provides a novel model called Matrix Plane Model (MPM), which can capture the best-fit window size dynamically and automatically. Also, we set up an experiment to compare MPM with some widely used measures by applying to semantic relatedness measures. The results show that our approach makes significant improvement in performance of semantic relatedness measures.
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