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A Graph based Semi Supervised Learning for Question Answering

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Figure

Figure 1: Graph Summarization. (a) Actual data point with predicted class labels, (b) magnified view ofa single node (black) and its boundaries (c) calculated representative vertex, (d) summary dataset.
Table 1: MRR for different features and methods.
Table 2: The MRR (%) results of graph-summary SSL (gSum SSL) and SVM as well as Hybrid gSumSSL and Hybrid SVM with different sizes of labeled data.

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