Abstract: Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.
Abstract: The paper shows that on transferring sense from the
SL to the TL, the translator’s reading against the grain determines the
creation of a faulty pattern of rendering the original meaning in the
receiving culture which reflects the use of misleading transformative
codes. In this case, the translator is a writer per se who decides what
goes in and out of the book, how the style is to be ciphered and what
elements of ideology are to be highlighted. The paper also proves that
figurative language must not be flattened for the sake of clarity or
naturalness. The missing figurative elements make the translated text
less interesting, less challenging and less vivid which reflects poorly
on the writer. There is a close connection between style and the
writer’s person. If the writer’s style is very much altered in a
translation, the translation is useless as the original writer and his /
her imaginative world can no longer be discovered. The purpose of the paper is to prove that adaptation is a dangerous
tool which leads to variants that sometimes reflect the original less
than the reader would wish to. It contradicts the very essence of the
process of translation which is that of making an original work
available in a foreign language. If the adaptive transformative codes
are so flexible that they encourage the translator to repeatedly leave
out parts of the original work, then a subversive pattern emerges
which changes the entire book. In conclusion, as a result of using adaptation, manipulative or
subversive effects are created in the translated work. This is generally
achieved by adding new words or connotations, creating new figures
of speech or using explicitations. The additional meanings of the
original work are neglected and the translator creates new meanings,
implications, emphases and contexts. Again s/he turns into a new
author who enjoys the freedom of expressing his / her own ideas
without the constraints of the original text. Reading against the grain
is unadvisable during the process of translation and consequently,
following personal common sense becomes essential in the field of
translation as well as everywhere else, so that translation should not
become a source of fantasy.
Abstract: Tamil handwritten document is taken as a key source
of data to identify the writer. Tamil is a classical language which has
247 characters include compound characters, consonants, vowels and
special character. Most characters of Tamil are multifaceted in
nature. Handwriting is a unique feature of an individual. Writer may
change their handwritings according to their frame of mind and this
place a risky challenge in identifying the writer. A new
discriminative model with pooled features of handwriting is proposed
and implemented using support vector machine. It has been reported
on 100% of prediction accuracy by RBF and polynomial kernel based
classification model.
Abstract: Tamil handwritten document is taken as a key source of data to identify the writer. Tamil is a classical language which has 247 characters include compound characters, consonants, vowels and special character. Most characters of Tamil are multifaceted in nature. Handwriting is a unique feature of an individual. Writer may change their handwritings according to their frame of mind and this place a risky challenge in identifying the writer. A new discriminative model with pooled features of handwriting is proposed and implemented using support vector machine. It has been reported on 100% of prediction accuracy by RBF and polynomial kernel based classification model.