... G(i−2, j
2
, k)
× P (j
1
|j
2
) × P(l|j
2
j
1
)).
end for
end for
for i = 4 to r do
for (l, k) such that σ ∪ {k : c, l : C
i
} is con-
sistent do
for j
1
, j
2
such that σ ∪ {k : c, j
2
:
C[i−2], ... G to 0.
i = 1
for All (l, k) such that σ ∪ {k : c, l : C
i
} is
consistent do
G(i, l, k) = P (l).
end for
i = 2
for All (l, k) such that σ ∪ {k : c, l : C
i
} is
consistent do
for...
... Empirical Methods in Nat-
ural Language Processing, pages 9–16, Philadel-
phia, July 2002. SIGDAT, Association for Com-
putational Linguistics.
Taku Kudo and Yuji Matsumoto. Fast methods
for kernel-based ... this, we describe a full model for WSD
built on KPCA. We then discuss experimental re-
sults confirming that this model outperforms state-
of-the-art published models for Senseval-...
... pattern matching method for
compiling a bilingual lexicon of nouns and
proper nouns from unaligned, noisy paral-
lel texts of Asian/Indo-European language
pairs. Tagging information of one ... terms
are nouns, proper nouns or noun phrases, compiling
a bilingual lexicon of these word groups is an impor-
tant first step.
We have been studying robust lexicon compilation
methods wh...
... BLEU4.
For the phrase- based system, phrases are of at
most 7 words on either source or target-side. For
the hierarchical phrase- based system, all SCFG
rules have at most two variables. For the ... the toolkit.
3.1 Phrase Extraction and Reordering Model
We use a standard way to implement the phrase
extraction module for the phrase- based model.
That is, we extract all...
... m
j
represents the jth mention (e.g., m
6
for the pronoun
“he”). e
i j
represents the partial entity i before the
jth mention. For example, e
1 6
denotes the part of
e
1
before m
6
, i.e., {“Microsoft Corp.”, ... is a bare noun
phrase with the same head string as B, and matches
in number with B. In this way, the detailed informa-
tion of each individual mention in an entity can be
cap...
... not enough for the WSD model-
ing. The lack of training information leads to a
low performance of the supervised methods.
3) With a large-scale training corpus, the un-
supervised WSD method has ...
mathematical modeling methods can be applied
to EP-based WSD methods. This section focuses
on the application of the EP concept to WSD,
and chooses Bayesian method for the classifier...
...
satisfying:
• CS(N) + C_ CS(N),
for each N;
• (N, L) • CS(N),
for each N such that N <~*
l, and each L • Af*;
• N • CS(N),
for each N such that -~(N<~*l);
and
• for each
N, children(N) ... needed for construction of parse trees
(or "derived trees" as they are often called for
TAGs) and the computation of features are al-
most identical to the correspondi...
...
i
:=
O;
forall z E F
do
create
uarae-concat¢~atoT'-Rmb age~|
J~f i;
N, ~= s*lve(x); i := i + 1;
forellend
for j := 0 to i
do
R := R
U
(Wait-lor-result(J~fj));
forend
return ... need for real-time NLP systems has been
discussed for the last decade. The difficulty in
implementing such a system is that people can
not use sophisticated but computationally ex-
pensiv...
... within
them. In cases where one phrase has more skipped
words than the other, the phrase with more skipped
words is discarded in favor of the more complete parsed
phrase. This operation significantly ... fea-
tures are designed to be general and, for the most part,
grammar and domain independent. For each parse, the
heuristic computes a penalty score for each of the fea-
tur...