... term.
3. There is a definition or explanation of the term.
4. There are several technical terms that are re-
lated to the term.
We have implemented the checking program of the
first two conditions in the ... 000
then x is a technical term.
2.3.2 Relation test
The relation test removes the terms that are not
closely related to the seed term from the candidates.
Our...
... '~vhat you then do is you make
them
think (These examples are actual text
from the Penn corpus.)
The
extraordinary accuracy of verb detection
within a tiny fraction of the rate achieved ... (1980). The completeness of the output
list increases monotonically with the total number
of occurrences of each verb in the corpus. False
positive rates are one to th...
... Statistics of the BioScope corpus. The 2nd and 3d
columns show the total number of cues within the datasets; the
4th and 5th columns show the percentage of negated and spec-
ulative sentences.
70% of the ... by matching the path of cue
leaf nodes to the root of the rule subtree pattern. If an
identical path exists in the sentence, the root of the
candid...
... H) is then in entailment when
sim(T, H) > α. These approaches can hardly
determine whether the entailment holds in the ex-
amples of the previous section. From the point of
view of bag -of- word ... their children to the risk of sun dam-
age, thinking they are better protected
than they actually are.”
H “Ron Gainsford is the chief executive of
the TSI.”
Only the...
... Learning the polarity of words
There are some works that discuss learning the po-
larity of words instead of sentences.
Hatzivassiloglou and McKeown proposed a
method of learning the polarity of adjectives ... closely related to
our work, there is a striking difference. In those
researches, the target is limited to the polarity of
words and none of them discussed sen...
... covers most of the words in the phrase. Then
the system annotates this sentence with the ap-
propriate “pro” or “con” label. All remaining
sentences with neither label are marked as “nei-
ther”. ... of
the unprofessional, rude, ob-
noxious, and unsanitary treat-
ment from the employees.
(2) They never get my order
right the first time and what
really disgusts me is how...
... trees rather than parts of speech.
CFG We extracted a CFG from the ∼10% of the
Penn Treebank found in the NLTK-lite corpora.
7
This CFG was then augmented with productions de-
rived from the PoS-tagged ... instance, the sequence-size 8 example was
constructed by stringing together the three consecu-
tive sequences of length 8 (There to; be have;
to ) taken from the c...
... construction of N-best translation
lexicons from parallel text. Melamed (1995) used
the ratio (LCSR) between the length of the LCS of
two words and the length of the longer word of the
two words ... sequence of words. The
intuition is that the longer the LCS of two transla-
tions is, the more similar the two translations are.
We propose using LCS-bas...
... contains
the collocation for the sense about the position of
professor’. Another cluster in the white area is the
cluster for the sense about ‘furniture’. The words
in each cluster are the representative ... express the
hidden features into the surface of the context
vector.
3.1 Discovering sense boundary
We discovered the senses of the homonyms with
clu...
... something.
The task of the system described in the follow-
ing is to identify and filter out nonreferential in-
stances of it, like the first and second one in the
example. By preventing these instances from ... available about whether an
instance of it is referential or not.
The remainder of this paper is structured as fol-
lows: Section 2 describes the current state o...