In this paper, we propose to develop a Story Generator from hints using a machine learning approach. During the learning phase, the system is fed with stories which are POS tagged and are converted into a Language Relationship model that is represented as a conceptual graph. During the synthesis phase, the input hints which are delimited using hyphen and converted to a conceptual graph. This graph is matched with the conceptual graph of the corpus and probable words, its sequences along with the relationship are determined using three proposed methods namely Randomized selection, Weighted Selection using Bigram Probability of hint phrases and Weighted Selection using product of Bigram Probability of Conceptual Graph and Bigram Probability of hint phrases. Using the words, sequences and relationships, a sentence assembler algorithm is designed to position the words to form a sentence. To make the story complete and readable, suffixes are added using Tamil grammar to the assembled words and a story is generated which is syntactically and semantically correct.
Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach
