... finding all
for a given is . Therefore,
the total cost is .
For all non-empty , we create a new state and
for all we set . We create a transition
, and for all such that ,
we set . For all such ... Generalized Algorithms for Constructing Statistical Language Models
Cyril Allauzen, Mehryar Mohri, Brian Roark
AT&T Labs – Research
180 ... in a general software library
for...
... known method
for estimating N-gram language models.
Kneser-Ney smoothing, however, requires
nonstandard N-gram counts for the lower-
order models used to smooth the highest-
order model. For some ... schema, C
n
denotes the counting method
used for N-grams of length n. For most smoothing
methods, C
n
denotes actual training corpus counts
for all n. For KN smoothing and its var...
... several tabular algorithms
for Tree Adjoining Grammar parsing,
creating a continuum from simple pure
bottom-up algorithms to complex pre-
dictive algorithms and showing what
transformations must ...
resenting structure. Several parsing algorithms
have been proposed for this formalism, most of
them based on tabular techniques, ranging from
simple bottom-up algorithms (Vij...
... translations for large do-
mains. Hence, in many applications, post-editing
'The author is now affiliated with the Information Science
Institute, University of Southern California, och@isi.edu
.
of ... prototype sys-
tem.
2 Statistical Machine Translation
We are given a source language ('French') sen-
tence =
f3
. . .
ff,
which is to be trans-
lated into a target lan...
... ways:
only for word selection, as a frequency measure,
or also for word representation, as a mapping for
common words. In the former, we preserve in-
flected variants that may be useful to model the
language ... LMs for
the target language modeling component of
a phrase-based statistical machine transla-
tion system.
1 Introduction
The translation of TED conference talks
1
is an...
... Language Processing.
Marcello Federico and Mauro Cettolo. 2007. Efficient
handling of n-gram language models for statistical ma-
chine translation. In Proceedings of the Second Work-
shop on Statistical ... all value ranks for a
given language model will vary – we will refer to
this variable as v .
2.2 Trie-Based Language Models
The data structure of choice for the majority o...
... to
natural language understanding and is a useful in-
termediate step for many other language process-
ing tasks (Ide and Veronis, 1998). Many recent
approaches make use of ideas from statistical ... thus
providing huge resources of labeled data for super-
vised approaches to make use of.
For the rest of this paper, for simplicity we will
refer to the primary language of the p...
... = 0.001, which
we tuned for best performance on the test set, giving an unfair
advantage to our competitor.
Finally, there are some methods that use auxil-
iary tasks for training sequence models, ... their associated majority label.
Features for each label were chosen by the method de-
scribed in HK06 – top frequency for that label and not
higher frequency for any other label.
+ SV...
... you will never cover all the things
that might reasonably be said. Language is
often too rich for the task being performed;
for example it can be difficult to establish that
two documents are d ... problem within
language processing is the over-specificity of
language, and the sparsity of data. Corpus-
based techniques depend on a sufficiency of
examples in order to model human language...
... permutation for
every vector (by choosing random values for a
and b, q number of times). Thus for every vec-
tor we have q different bit permutations for the
original bit stream.
5. For each permutation ... Association for Computational Linguistics
Randomized Algorithms and NLP: Using Locality Sensitive Hash Function
for High Speed Noun Clustering
Deepak Ravichandran, Patrick...