Sentiment analysis (SA) is an important research area in cognitive computation—thus, in-depth studies of patterns sentiment are necessary. At present, rich-resource data-based SA has been well-developed, while the more challenging and practical multi-source unsupervised (i.e., a target-domain by transferring from multiple source domains) seldom studied. The challenges behind this problem mainly...

Unsupervised morphological analysis is the task of segmenting words into prefixes, suffixes and stems without prior knowledge of language-specific morphotactics and morpho-phonological rules. This paper introduces a simple, yet highly effective algorithm for unsupervised morphological learning for Bengali, an Indo-Aryan language that is highly inflectional in nature. When evaluated on a set of ...

We study a number of natural language decipherment problems using unsupervised learning. These include letter substitution ciphers, character code conversion, phonetic decipherment, and word-based ciphers with relevance to machine translation. Straightforward unsupervised learning techniques most often fail on the first try, so we describe techniques for understanding errors and significantly i...