Improving graph-based random walks for complex question answering using syntactic, shallow semantic and extended string subsequence kernels

The task of answering complex questions requires inferencing and synthesizing information from multiple documents that can be seen as a kind of topic-oriented, informative multi-document summarization. In generic summarization the stochastic, graph-based random walk method to compute the relative importance of textual units (i.e. sentences) is proved to be very successful. However, the major limitation of the TF*IDF approach is that it only retains the frequency of the words and does not take into account the sequence, syntactic and semantic information. This paper presents the impact of syntactic and semantic information in the graph-based random walk method for answering complex questions. Initially, we apply tree kernel functions to perform the similarity measures between sentences in the random walk framework. Then, we extend our work further to incorporate the Extended String Subsequence Kernel (ESSK) to perform the task in a similar manner. Experimental results show the effectiveness of the use of kernels to include the syntactic and semantic information for this task.

Research Highlights
► Graph-based random walk is successful to find relative importance of sentences.
► TF*IDF disregards the sequence, syntactic and semantic information.
► Impact of syntactic and semantic information in graph-based random walk.
► Syntactic tree and/or shallow semantic tree and ESSK outperform TF*IDF.
► Shallow semantic tree performs the best to answer complex questions.