6,792 research outputs found
Molding CNNs for text: non-linear, non-consecutive convolutions
The success of deep learning often derives from well-chosen operational
building blocks. In this work, we revise the temporal convolution operation in
CNNs to better adapt it to text processing. Instead of concatenating word
representations, we appeal to tensor algebra and use low-rank n-gram tensors to
directly exploit interactions between words already at the convolution stage.
Moreover, we extend the n-gram convolution to non-consecutive words to
recognize patterns with intervening words. Through a combination of low-rank
tensors, and pattern weighting, we can efficiently evaluate the resulting
convolution operation via dynamic programming. We test the resulting
architecture on standard sentiment classification and news categorization
tasks. Our model achieves state-of-the-art performance both in terms of
accuracy and training speed. For instance, we obtain 51.2% accuracy on the
fine-grained sentiment classification task
Telugu Text Categorization using Language Models
Document categorization has become an emerging technique in the field of research due to the abundance of documents available in digital form. In this paper we propose language dependent and independent models applicable to categorization of Telugu documents. India is a multilingual country; a provision is made for each of the Indian states to choose their own authorized language for communicating at the state level for legitimate purpose. The availability of constantly increasing amount of textual data of various Indian regional languages in electronic form has accelerated. Hence, the Classification of text documents based on languages is crucial. Telugu is the third most spoken language in India and one of the fifteen most spoken language n the world. It is the official language of the states of Telangana and Andhra Pradesh. A variant of k-nearest neighbors algorithm used for categorization process. The results obtained by the Comparisons of language dependent and independent models
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