... state -of- the-art machine
learning systems. Additionally, the intuitive
and linguistically motivated rules will allow
for manual adaptation of the rule set to new
domains and corpora.
1 Motivation
Information ... 35.92, and
65.55 for clinical texts, papers, and abstracts respec-
tively.
Morante and Daelemans have also developed a
metalearner for identifying the scope of neg...
... designed an effective way
to automatically learn entailment rules from ex-
amples and (b) our approach is highly accurate and
exceeds the accuracy of the current state -of- the-art
401
models (Glickman ... on a intra-pair similarity between T and
H but also on a cross-pair similarity between two
pairs (T
, H
) and (T
, H
). The latter similarity
measure along with aset...
... result.
Classifier and data sets As a classifier, we
chose Naive Bayes with bag -of- words features,
because it is one of the most popular one in this
task. Negation was processed in a similar way as
previous ... keisan-ga yoininaru
cost computation-
POST become easy
It becomes easy to compute cost.
kantan-de jikan-ga setsuyakudekiru
easy-
POST time-POST can save
It’s easy and can sav...
... accuracy, precision, and recall of the
system on each test set. We calculated numbers
in each A and B column by assuming each anno-
tator’s answers separately as a gold standard.
In Table ... “preferential treatment given to large
groups”, and “they don't offer salads of any
kind” are hard to predict. Also, they seem rarely
share common keyword features.
We first...
... 2109 Australia NSW 2109 Australia
madras@ics.mq.edu.au
Abstract
In evaluating the output of language tech-
nology applications—MT, natural language
generation, summarisation—automatic eval-
uation ... in applying SVMs here are, first, noting that
human translations are generally good and machine
translations poor, that binary training data can be
created by taking the human translations as...
... 0.83
With Case Information (Case) Lower Case (NoCase) Lower Case & Stemmed (Stem)
With Case Information (Case) Lower Case (NoCase) Lower Case & Stemmed (Stem)
Table 1. Pearson’s ρ and Spearman’s ... unigram and bi-
gram, i.e. N=2, for the purpose of explanation and
call this B
LEU-2. Using S1 as the reference and S2
and S3 as the candidate translations, S2 an...
...
collocation. A target word for collocation is called
the ‘central word , and a word in a collocation is
referred to as the ‘contextual word . ‘Surrounding
words’ mean the collocation for all contextual ...
noise and trivial collocation. We call this process
normalization, and it is specifically provided as [8].
The statistically unrelated words can be said that
the w...
... System configuration
Automatic acquisition of technical terms in a cer-
tain domain has been studied as automatic term
recognition (Kageura and Umino, 1996; Kageura
and Koyama, 2000), and the methods ... col-
lect technical terms that are related to natural lan-
guage processing, such as morphological analysis,
parsing, information retrieval, and machine transla-
tion. The target a...
... immediately to the right
of a main verb. Adverbs and adverbial phrases
(including days and dates) are ignored for the pur-
poses of case adjacency. A noun-phrase that sat-
isfies the Case Filter ... examples are clear and unambiguous.
• Observations made in clear cases generalize
to all cases.
• It is possible to distinguish the clear cases
from the ambiguous ones wit...
... .58
Table 1: Classification of it by two annotators in a corpus subset.
4 Automatic Classification
4.1 Training and Test Data Generation
4.1.1 Segmentation
We extracted all instances of it and the ... shallow feature generation meth-
ods could propagate into the model that was
learned from the data. The advantage of this ap-
proach is, however, that training and test data are
ho...