Enhanced Model to Improve Memory Based Learning Algorithm
Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 6
Abstract
Abstract: Opinion mining/Sentiment analysis is a field of research which focuses on tracking human opinionswritten in natural language. Many companies, Now-a-days, extract opinions from various internet sources (suchas review sites, blogs, twitter) and try to predict customer's reviews on their upcoming products. Parts-OfSpeechTagging or POS Tagging, being a crucial step in the process of opinion mining, is the assigning of eachword with its POS tag, according to its definition and context in the corpus. In this paper, we will first havedetailed study of a POS tagging algorithm i.e. Memory Based Learning Algorithm, along with other POStagging techniques and then, dealing with its limitations, we will propose an enhanced model in order tosignificantly improve the efficiency of Memory-Based Learning Algorithm. The Memory Based LearningAlgorithm, maintains a word-tag-feature repository, storing word with all its possible tags, along with its fixedwidth context called feature. During tagging, the algorithm retrieves/computes an appropriate tag, deduced byanalyzing feature value of the word. Thus, this algorithm is ineffective, when dealing with sparse data. To beable to deal with this limitation, we have devised an enhanced Memory Based Learning model. In addition tothis, we have compared the proposed model with other POS tagging algorithm, in terms of its performance.
Authors and Affiliations
Aastha Gupta
Keywords
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