Information Technology Reference
In-Depth Information
Fig. 6.9 Average precision scores for language model parameters: relevance feedback from
hypertext to Semantic Web
performing below the baseline, although its performance increased as the window
size increased, reaching an average precision of 0.6262 with m
=
,
<
.
05).
However, given that a window size of 10,000 covered most documents, increasing
the window size will not likely result in better performance from lca .The ponte
relevance feedback performed very well, reaching a maximum MAP 0.8756 with
a window size of 300 using inquery weighing, and so was insignificantly different
from inquery ( p
3
000 ( p
0
>
.
05). Lastly, both ponte and okapi experienced a significant
decrease in performance as m was increased, so it appears that the window sizes
of 300 and 100 are indeed optimal. Also, as regards comparing baselines, inquery
outperformed cosine ( p
0
<
.
05).
For language models, both averaged relevance models rm and concatenated
relevance models tf were investigated, with the primary parameter being m ,the
number of non-zero probability words used in the relevance model. The parameter
m was varied between 100, 300, 1,000, 3,000,and 10,000. Remember that the query
model is the relevance model for the language model-based frameworks. As is
best practice in relevance modeling, the relevance models were not smoothed, but
a number of different smoothing parameters for
0
ε
were investigated for the cross
entropy ranking function, ranging from
ε
between 0.01, 0.1, 0.2, 0.5, 0.8, 0.9, and
0.99. The results are given in Fig. 6.9 .
Search WWH ::




Custom Search