Abstract Research into representation learning models of lexical semantics usually utilizes some form intrinsic evaluation to ensure that the learned representations reflect human semantic judgments. Lexical similarity estimation is a widely used method, but efforts have typically focused on pairwise judgments words in isolation, or are limited specific contexts and stimuli. There limitations w...

Textual documents are growing rapidly through the internet in today’s modern technology era. Electronic structured databases archive offline and online documents, e-mails, webpages, blog social network posts. Without appropriate ranking demand clustering when there is classification without any specifics, it quite difficult to retain access these documents. K-means one of methods that frequentl...

Search is the most obvious application of information retrieval. The variety of widely obtainable biomedical data is enormous and is expanding fast. This expansion makes the existing techniques are not enough to extract the most interesting patterns from the collection as per the user requirement. Recent researches are concentrating more on semantic based searching than the traditional term bas...

MOTIVATION Clustering MEDLINE documents is usually conducted by the vector space model, which computes the content similarity between two documents by basically using the inner-product of their word vectors. Recently, the semantic information of MeSH (Medical Subject Headings) thesaurus is being applied to clustering MEDLINE documents by mapping documents into MeSH concept vectors to be cluster...