2 research outputs found
Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet
Word embedding approaches increased the efficiency of natural language processing (NLP) tasks. Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words. The tasks of semantic similarity between concepts need to understand relations like hypernymy and synonym sets to produce efficient word embeddings. The outcomes of any expert system are affected by the text representation. Systems that understand senses, context, and definitions of concepts while deriving vector representations handle the drawbacks of single vector representations. This paper presents a novel idea for handling polysemy by generating Multi-Sense Embeddings using synonym sets and hypernyms information of words. This paper derives embeddings of a word by understanding the information of a word at different levels, starting from sense to context and definitions. Proposed sense embeddings of words obtained prominent results when tested on word similarity tasks. The proposed approach is tested on nine benchmark datasets, which outperformed several state-of-the-art systems
Revitalising the Single Batch Environment: A 'Quest' to Achieve Fairness and Efficiency
In the realm of computer systems, efficient utilisation of the CPU (Central
Processing Unit) has always been a paramount concern. Researchers and engineers
have long sought ways to optimise process execution on the CPU, leading to the
emergence of CPU scheduling as a field of study. This research proposes a novel
algorithm for batch processing that operates on a preemptive model, dynamically
assigning priorities based on a robust ratio, employing a dynamic time slice,
and utilising periodic sorting technique to achieve fairness. By engineering
this responsive and fair model, the proposed algorithm strikes a delicate
balance between efficiency and fairness, providing an optimised solution for
batch scheduling while ensuring system responsiveness