Due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the imp...

This work concerns the search for text compressors that compress better than existing dictionary coders, but run faster than statistical coders. We describe a new method for text compression using multiple dictionaries, one for each context of preceeding characters, where the contexts have varying lengths. The context to be used is determined using an escape mechanism similar to that of predict...

We are proposing a format for translation dictionaries suitable for machine translation. The dictionary format is concise and generalizes phrases by introducing rules for morphological generation instead of using simple phrase to phrase mapping. We describe a simple way how to automatically construct our compact entries from a machine-readable dictionary originally intended for human users usin...

We propose a novel method to expand a small existing translation dictionary to a large translation dictionary using a pivot language. Our method depends on the assumption that it is possible to find a pivot language for a given language pair on condition that there are both a large translation dictionary from the source language to the pivot language, and a large translation dictionary from the...

This letter considers the problem of dictionary learning for sparse signal representation whose atoms have low mutual coherence. To learn such dictionaries, at each step, we first update the dictionary using the method of optimal directions (MOD) and then apply a dictionary rank shrinkage step to decrease its mutual coherence. In the rank shrinkage step, we first compute a rank 1 decomposition ...

Any machine translation system requires a transfer dictionary between the source and target languages. Typically, since the construction of such a dictionary is done by hand, a lot of time is taken and the cost is enormous. Considering this, we attempted the construction of a bilingual dictionary through the re-generation of already-existing language resources. Aiming at the generation of a Kor...

In this paper, we propose a new sampling strategy for hyperspectral signals that is based on dictionary learning and singular value decomposition (SVD). Specifically, we first learn a sparsifying dictionary from training spectral data using dictionary learning. We then perform an SVD on the dictionary and use the first few left singular vectors as the rows of the measurement matrix to obtain th...