The traditional Point-wise Mutual Information (PMI) method has shortcoming of overvaluing the co-occurrence of two low-frequency words. To get the proper value of k of improved PMI named PMI^k to overcome the shortcoming of PMI, and solve the problem that the term extraction cannot be obtained from a segmented corpus with segmentation errors, as well as maintaining the portability of term extraction system, combining with the PMI^k method and two fundamental rules, a new method was put forward to identity terms from an unsegmented corpus. Firstly, 2-gram extended seed was determined by computing the bonding strength of two adjoining words by PMI^k method. Secondly, whether the 2-gram extended seed could be extended to 3-gram was determined by respectively computing the bonding strength between the seed and the word in front of it and the word located behind it, and then getting multi-gram term candidates iteratively. Finally, the garbage of term candidates were filtered using the two fundamental rules to obtain terms. The theoretical analysis shows that PMI^kcan overcome the shortcoming of PMI when k≥3(k∈N₊). The experiments on 1 GB SINA finance Blog corpus and 300 MB Baidu Tieba corpus verify the theoretical analysis, and PMI^k outperforms PMI with good portability.