107 research outputs found
Are Word Embedding-based Features Useful for Sarcasm Detection?
This paper makes a simple increment to state-of-the-art in sarcasm detection
research. Existing approaches are unable to capture subtle forms of context
incongruity which lies at the heart of sarcasm. We explore if prior work can be
enhanced using semantic similarity/discordance between word embeddings. We
augment word embedding-based features to four feature sets reported in the
past. We also experiment with four types of word embeddings. We observe an
improvement in sarcasm detection, irrespective of the word embedding used or
the original feature set to which our features are augmented. For example, this
augmentation results in an improvement in F-score of around 4\% for three out
of these four feature sets, and a minor degradation in case of the fourth, when
Word2Vec embeddings are used. Finally, a comparison of the four embeddings
shows that Word2Vec and dependency weight-based features outperform LSA and
GloVe, in terms of their benefit to sarcasm detection.Comment: The paper will be presented at Conference on Empirical Methods in
Natural Language Processing (EMNLP) 2016 in November 2016.
http://www.emnlp2016.net
Offline Handwriting Recognition Using Genetic Algorithm
In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for
handwriting segmentation has been described here with the help of which individual characters can be
segmented from a word selected from a paragraph of handwritten text image which is given as input to the
module. Then each of the segmented characters are converted into column vectors of 625 values that are later
fed into the advanced neural network setup that has been designed in the form of text files. The networks has
been designed with quadruple layered neural network with 625 input and 26 output neurons each corresponding
to a character from a-z, the outputs of all the four networks is fed into the genetic algorithm which has been
developed using the concepts of correlation, with the help of this the overall network is optimized with the help of
genetic algorithm thus providing us with recognized outputs with great efficiency of 71%
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