... estimate
search results diversity using our sense inven-
tories?
3. Sense frequencies: knowing sense frequen-
cies in (search results) Web pages is crucial
to have a usable sense inventory. Is ... found
in the page for the sense being trained.
• TiMBL-inlinks uses the examples found in
Wikipedia pages pointing to the sense being
trained.
• TiMBL-all uses both sou...
... having sufficient power are therefore
extremely important in minimizing the in uence of
confounding factors arising from inherent problems
associated with breeding APP transgenic mice and
maintaining ... the N-terminus of the Ab domain in APP. It
was identified in 1992 [47] and shown to increase Ab
levels by six- to eightfold [4]. These discoveries created
intense interest in APP p...
... special tweaking in model training. The ranking
model can not only be used in a pre-reordering based
SMT system, but also be integrated into a phrase-
based decoder serving as additional distortion ... simple binary classification problem,
which can be easily solved by a two-class linear sup-
port vector machine.
4 Integration into SMT system
There are two ways to integrate the ranking...
... extraction (including training and testing processes)
In the training process, for every entity type in
the ACE training corpus, a clustering technique
(CLUTO toolkit)
3
is used to divide it into ... support vector ma-
chine (SVM) based classifiers are also trained:
y Argument Classifier: to distinguish arguments
of a potential trigger from non-arguments
4
;
y Role Classifier:...
...
easily integrated into existing Chinese input
systems by identifying words as a post process-
ing. Our experimental results show that, by ap-
plying the WSM as an adaptation processing
together ... corresponding pinyin sylla-
ble-words. The pinyin syllable-words were
translated by phoneme -to- pinyin mappings, such
as “ㄩˊ” -to- “
ju2.”
2.1 Auto-Generation of WP Database
Following...
... are added to the training set pertaining to the
corresponding model. The label of each such feature
vector consists of the target word and the correspond-
ing sense, represented as word #sense. Table ... models
SenseLearner
definitions
Word sense
disambiguation
Trained semantic
models
Sense tagged
texts
Figure 1: Semantic model learning in SENSE-
LEARNER
tagged texts, including...
... A. Hussain, C. Havasi, and C. Eckl. 2010a.
Senticspace: visualizing opinions and sentiments in
a multi-dimensional vector space. Knowledge-Based
and Intelligent Information and Engineering Systems,
pages ... Hu-
mor modeling in the interface. In CHI’03 extended ab-
stracts on Human factors in computing systems, pages
1050–1051. ACM.
V. Raskin. 1998. The sense of humor and the truth....
... is based on redefining the task
of lemmatisation as a category tagging task. Formu-
lating lemmatisation as a tagging task allows the use
of advanced tagging techniques, and the efficient in-
tegration ... Lemmatisation as a Tagging Task
Lemmatisation is the task of grouping together word
forms that belong to the same in ectional morpho-
logical paradigm and assigning to each parad...
... et al., 2004;
Petcu and Faltings, 2005) . As the number of
constraints or words increase, the search space in-
creases thereby increasing the time and memory
bounds to solve them. Also DCOP algorithms ... bank refers to
financial institution sense rather than the edge of
a river sense. The word cancer has at least two
senses, one corresponding to the astrological sign
and the ot...
... noun phrases to
refer to entities the hearer already knows about and can
identify, and indefinite noun phrases to refer to new enti-
ties the speaker is introducing. However, in the casually ... information is presented, linguistically.
The main verb is more likely to convey new information
than a definite noun phrase. Thus, we assign a cost to
each of the atoms the cost o...