A Tensor based Factorization Model of Semantic Compositionality

This proposed model mainly consisted of two main agents: indexing agent that carries out enhanced Latent Dirichlet Allocation algorithm (E-LDA), and ranking agent

This graph shows that the TE model achieves a much higher correlation with human judged similarity scores (with an average correlation of 0.74 over all datasets and corpora) than

A rigorous evaluation of eight different corpus- based approaches on the task of judging med- ical concept similarity found that the best per- formance was achieved using a

The approaches for computing semantic similarity between text snippets can be broadly classified into three approaches: information retrieval vector space model

However, because word vectors from the CBOW model are trained assuming additive interactions between context words, the word composition used for the training mismatches to

Abstract: The Tensor Product (TP) model transformation is a recently proposed technique for transforming given Linear Parameter Varying (LPV) state-space models into polytopic

In Section 2 on Task Model for Interactive Application, we present a model of task using events, and show how complex tasks can be represented using event abstractions and relations

This verb argument structures are captured and analysed in document and corpus level using concept based mining model.. In this model the arguments and verbs are considered