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

Yllias Chali, Sadid A. Hasan, Shafiq Rayhan Joty

Research output: Contribution to journalArticle

17 Citations (Scopus)


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 TFIDF 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.

Original languageEnglish
Pages (from-to)843-855
Number of pages13
JournalInformation Processing and Management
Issue number6
Publication statusPublished - 1 Nov 2011
Externally publishedYes



  • Complex question answering
  • Extended string subsequence kernel
  • Graph-based method
  • Shallow semantic kernel
  • Syntactic kernel

ASJC Scopus subject areas

  • Media Technology
  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences
  • Management Science and Operations Research

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