section 2.3).
The objective of our work was to present what
The features mentioned above determined a space
we believe is a valuable conceptual framework;
Ψ; An answer a, in such feature space, assumed
more advance machine learning and summariza-
the vectorial form:
tion techniques would most likely improve the per-
Ψ a = ( ϑ, ς, $, % )
formances.
The remaining of this paper is organized as fol-
Following the intuition that chosen best answers
lows. In the next section Quality, Coverage, Rel-
(a ? ) carry high quality information, we used su-
evance and Novelty measures are presented; we
pervised ML techniques to predict the probability
explain how they were calculated and combined
of a to have been selected as a best answer a ? . We
to generate a final summary of all answers to a
trained a Linear Regression classifier to learn the
question. Experiments are illustrated in Section
weight vector W = (w 1 , w 2 , w 3 , w 4 ) that would
3, where we give evidence of the effectiveness of
combine the above feature. Supervision was given
our method. We list related work in Section 5, dis-
in the form of a training set T r Q of labeled pairs
cuss possible alternative approaches in Section 4
defined as:
and provide our conclusions in Section 6.
T r Q = {h Ψ a , isbest a i}
2 The summarization framework
isbest a was a boolean label indicating whether a
2.1 Quality as a ranking problem
was an a ? answer; the training set size was de-
Quality assessing of information available on So-
termined experimentally and will be discussed in
cial Media had been studied before mainly as a
Bạn đang xem section 2. - BÁO CÁO KHOA HỌC METADATA AWARE MEASURES FOR ANSWER SUMMARIZATION IN COMMUNITY QUESTION ANSWERING PDF
