Semi Supervised Method for Detection of Ambiguous Word and Creation of Sense: Using WordNet

Abstract

Machine Translation, Information Retrieval and Knowledge Acquisition are the three main applications of Word Sense Disambiguation (WSD). The sense of a target word can be identified from a dictionary using a 'bag of words', i.e. neighbours of the target word. A target word has the same spelling of the word but with a different meaning, i.e. chair, light etc. In WSD, the key input sources are sentences and target words. But, instead of providing a target word, this should automatically be detected. If a sentence has more than one target word, then the filtration process will require further processing. In this study, the proposed framework, consisting of buzz words and query words has been developed to detect target words using the WordNet dictionary. Buzz words are defined as a 'bag-ofwords' using POS-Tags, and query words are those words having multiple meanings. The proposed framework will endeavor to find the sense of the detected target word using its gloss and with examples containing buzz words. This is a semi-supervised approach because 266 words of multiple meanings have been labelled from various sources and used based on an unsupervised approach to detect the target word and sense (meaning). After experimenting on a dataset consisting of 300 hotel reviews, 100 % of the target words for each sentence were detected with 84 % related to the sense of each sentence or phrase.

Multi-Head Self-Attention Gated-Dilated Convolutional Neural Network for Word Sense Disambiguation

“…Saqib designed a framework consisting of buzz words in 2018, in which query words had been developed to detect target words based on WordNet. The framework would find the sense of target word using its gloss and examples containing buzz words [13]. Cardellino proposed disjoint semi-supervised learning method, in which unsupervised model was trained on unlabeled data, and the result was used by supervised classifier [14].…”

Evaluation of Deep Learning Approaches for Sentiment Analysis

“…The proposed work is helpful in information analysis in the tweets where opinions are found heterogeneous, unstructured, polarized negative, positive, or neutral based on machine learning approach [42]. Most of the work has been done on words sense disambiguation, aspect extraction and aspect based sentiment analysis using text datasets [43,44]. Machine learning and deep learning techniques employ training data, whilst other techniques use different rules based on attributes and entities.…”

Graph Convolutional Network for Word Sense Disambiguation

“…Jain et al [24] gave a semisupervised algorithm for constructing WordNet graphs, in which clue words are used. Saqib et al [25] designed a framework consisting of buzz words and query words to use WordNet for detecting target words. Buzz words are defined as a 'bagof-words' using POS, and query words have multiple meanings.…”