  In this paper, we show that certain phrases although not present in a given
question/query, play a very important role in answering the question. Exploring
the role of such phrases in answering questions not only reduces the dependency
on matching question phrases for extracting answers, but also improves the
quality of the extracted answers. Here matching question phrases means phrases
which co-occur in given question and candidate answers. To achieve the above
discussed goal, we introduce a bigram-based word graph model populated with
semantic and topical relatedness of terms in the given document. Next, we apply
an improved version of ranking with a prior-based approach, which ranks all
words in the candidate document with respect to a set of root words (i.e.
non-stopwords present in the question and in the candidate document). As a
result, terms logically related to the root words are scored higher than terms
that are not related to the root words. Experimental results show that our
devised system performs better than state-of-the-art for the task of answering
Why-questions.
