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authordesaicwtf <desaicwtf@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-10-30 05:37:01 +0000
committerdesaicwtf <desaicwtf@ec762483-ff6d-05da-a07a-a48fb63a330f>2010-10-30 05:37:01 +0000
commit0da3df9b23f8fe588e8662f5be4ba4101bf0a8d4 (patch)
treecacd06f29bbb59a2baaf406cd74dc641fcb204d1
parent1305f38dba367f31754044cabc7b2bea9b8a2074 (diff)
added more numbers
git-svn-id: https://ws10smt.googlecode.com/svn/trunk@703 ec762483-ff6d-05da-a07a-a48fb63a330f
-rw-r--r--report/pr-clustering/posterior.tex44
1 files changed, 33 insertions, 11 deletions
diff --git a/report/pr-clustering/posterior.tex b/report/pr-clustering/posterior.tex
index eb53e915..ea8560c1 100644
--- a/report/pr-clustering/posterior.tex
+++ b/report/pr-clustering/posterior.tex
@@ -316,23 +316,43 @@ distribution
is obtained from Collins parser
trained on Penn Treebank. Since not
all phrases are constituents, we ignored
-phrases that don't correspond any constituents.
+phrases that don't correspond to any constituents.
+
+We conducted experiments with various data pre-processing
+and tried different models. Number of phrase categories
+is fixed at $25$. We chose to only look at
+the target side language. The context is set
+to be $1$ word to the left and to the right of the phrase.
+We chose such a setting because it emperically works better
+in the pipeline than other variations. This is also
+the case for non-parametric methods. The models as we discussed
+in previous sections are EM, EM with sparsity constraint,
+agreement of two models in reverse directions and agreement
+of two models trained on two languages. We tried our models
+with word classes as well. In the context, each word is
+replaced with a word class unsupervisedly learned from the data.
+The results are shown in Table \ref{tab:results}.
\begin{table}[h]
\centering
- \begin{tabular}{ |*{3}{c|} }
+ \begin{tabular}{ |*{4}{c|} }
\hline
- model & BLEU & H(Gold$|$Predicted)\\
+ \multicolumn{2}{|c|}{model} & BLEU & H(Gold$|$Predicted)\\
\hline
- hiero & 21.1 & 5.77\\
- hiero+POS & 22.3 & 1.00 \\
- SAMT & 24.5 & 0.00 \\
+ \multicolumn{2}{|c|}{hiero} & 21.1 & 5.77\\
+ \multicolumn{2}{|c|}{hiero+POS} & 22.3 & 1.00 \\
+ \multicolumn{2}{|c|}{SAMT} & 24.5 & 0.00 \\
\hline
- EM & 20.9 & 2.86 \\
- PR $\sigma=100$ & 21.7 & 2.36 \\
- agree language & 21.7 & 2.68 \\
- agree direction & 22.1 & 2.35\\
- non-parametric & 22.2 & ?\\
+ \multirow{2}{*}{EM} & words & 20.9 & 2.85 \\
+ & word classes & 21.54 & 2.86 \\ \hline
+ \multirow{2}{*}{PR $\sigma=100$}&words & 21.1 & 2.56 \\
+ &word classes & 21.7 & 2.36 \\ \hline
+ \multirow{2}{*}{agree language}&word & 21.7 & 2.80 \\
+ &word classes & 21.4 & 2.69\\ \hline
+ \multirow{2}{*}{agree direction}&word & 21.6 & 2.48\\
+ &word classes &22.1 &2.36 \\ \hline
+ \multirow{2}{*}{non-parametric}&word & 22.0 & 2.86\\
+ & word classes&22.3&2.27\\
\hline
\end{tabular}
\caption
@@ -356,6 +376,8 @@ in the beginning of this chapter. PR $\sigma=100$ is
posterior regularization model with sparsity constraint
explained in Section \ref{sec:pr-sparse}.
$\sigma$ is the constant controls strongness of the constraint.
+We picked $\sigma$ by trying different values ranging from
+$1$ to $100$.
Agree language and agree direction are models with agreement
constraints mentioned in Section \ref{sec:pr-agree}. Non-parametric
is non-parametric model introduced in the previous chapter.