Removal of Ambiguity of Noun Using Multimodal Approach

Abstract

It is truly amazing that human beings can easily understand and comprehend the intended meaning of an ambiguous word. The meaning of an ambiguous word differs with its usage in a different context. Still, human beings can Fig. out the meanings with ease. We have machine translation (MT) systems that can translate from a source language to its equivalent target language. The main intension of these MT systems is to seamlessly transfer the intended meaning of the source text to its target text. But due to ambiguous nature of natural language, MT systems do suffer from setbacks. Word sense disambiguation (WSD) is one of the greatest challenges to overcome. The researchers have contributed a number of WSD algorithms that operate over textual data. These algorithms were primarily developed to disambiguate an ambiguous word or to determine the exact meaning of an ambiguous word based on the context. It seems that context plays a decisive role while disambiguating an ambiguous word. A section of the research community are of the opinion that the neighbouring words that appear along with an ambiguous word in a sentence might help in finding the meaning or sense of an ambiguous word. This is commonly known as distributional semantics. In this paper, we have proposed a novel technique to remove the ambiguity of polysemous noun using a multimodal distributional semantics model (MDSM). The arduous task was to find a standard multimodal database for carrying out our desired experiments. This was compensated by using ImageNet database. ImageNet is a large-scale database containing tens of millions of annotated images organized by the semantic hierarchy of WordNet. Our MDSM exploits both the image features and textual features from the annotated images of ImageNet database. For both the training and testing purpose, we have used a total of 8 different synsets. The total no. of 800 related images to these synsets are used for training (each synset contains a reduced set of images i.e. 100 images). However, the total no. of images used for testing is 8 ((each synset contains 1 image) only. The 8 synsets that we have considered are {

Optimization Of Machining Parameters of CNC Milling Operation for Material Removal Rate and Surface Roughness on En-24 Steel Using Taguchi Method

“…The study also discovered that the most important factor determining MRR was cutting speed, whereas the most important factor affecting surface roughness was feed rate. [12] The goal of the study was to utilise the Taguchi technique and grey relational analysis to optimize the milling settings for AISI 304 austenitic stainless steel. The research revealed that a cutting speed of 100 m/min, a feed rate of 0.15 mm/rev, and a depth of cut of 0.2 mm were the ideal machining settings for producing the maximum MRR and the lowest surface roughness.…”