Automated Prediction of Good Dictionary EXamples (GDEX): A Comprehensive Experiment with Distant Supervision, Machine Learning, and Word Embedding‐Based Deep Learning Techniques
Abstract:Dictionaries not only are the source of getting meanings of the word but also serve the purpose of comprehending the context in which the words are used. For such purpose, we see a small sentence as an example for the very word in comprehensive book-dictionaries and more recently in online dictionaries. The lexicographers perform a very meticulous activity for the elicitation of Good Dictionary EXamples (GDEX)—a sentence that is best fit in a dictionary for the word’s definition. The rules for the elicitation … Show more
“…When an input is given to the random forest algorithm, each tree, based on its training, gives a classification for this input. The class with the majority of predictions is input predicted class 35 , 36 . Figure 4 Random forest 35 , 37 .…”
Section: Methodsmentioning
confidence: 99%
“…The class with the majority of predictions is input predicted class 35 , 36 . Figure 4 Random forest 35 , 37 . …”
Power Line Communication technology uses power cables to transmit data. Knowing whether a node is working in advance without testing saves time and resources, leading to the proposed model. The model has been trained on three dominant features, which are SNR (Signal to Noise Ratio), RSSI (Received Signal Strength Indicator), and CINR (Carrier to Interference plus Noise Ratio). The dataset consisted of 1000 readings, with 90% in the training set and 10% in the testing set. In addition, 50% of the dataset is for class 1, which indicates whether the node readings are optimum. The model is trained with multi-layer perception, K-Nearest Neighbors, Support Vector Machine with linear and non-linear kernels, Random Forest, and adaptive boosting (ADA) algorithms to compare between statistical, vector-based, regression, decision, and predictive algorithms. ADA boost has achieved the best accuracy, F-score, precision, and recall, which are 87%, 0.86613, 0.9, 0.8646, respectively.
“…When an input is given to the random forest algorithm, each tree, based on its training, gives a classification for this input. The class with the majority of predictions is input predicted class 35 , 36 . Figure 4 Random forest 35 , 37 .…”
Section: Methodsmentioning
confidence: 99%
“…The class with the majority of predictions is input predicted class 35 , 36 . Figure 4 Random forest 35 , 37 . …”
Power Line Communication technology uses power cables to transmit data. Knowing whether a node is working in advance without testing saves time and resources, leading to the proposed model. The model has been trained on three dominant features, which are SNR (Signal to Noise Ratio), RSSI (Received Signal Strength Indicator), and CINR (Carrier to Interference plus Noise Ratio). The dataset consisted of 1000 readings, with 90% in the training set and 10% in the testing set. In addition, 50% of the dataset is for class 1, which indicates whether the node readings are optimum. The model is trained with multi-layer perception, K-Nearest Neighbors, Support Vector Machine with linear and non-linear kernels, Random Forest, and adaptive boosting (ADA) algorithms to compare between statistical, vector-based, regression, decision, and predictive algorithms. ADA boost has achieved the best accuracy, F-score, precision, and recall, which are 87%, 0.86613, 0.9, 0.8646, respectively.
“…of estimators/trees used in the random forest is 500. The Gini impurity function has been employed while training the data for the algorithm ( Khan et al, 2021 ). Mean or average output from different trees is used to make predictions.…”
Human beings rely heavily on social communication as one of the major aspects of communication. Language is the most effective means of verbal and nonverbal communication and association. To bridge the communication gap between deaf people communities, and non-deaf people, sign language is widely used. According to the World Federation of the Deaf, there are about 70 million deaf people present around the globe and about 300 sign languages being used. Hence, the structural form of the hand gestures involving visual motions and signs is used as a communication system to help the deaf and speech-impaired community for daily interaction. The aim is to collect a dataset of Urdu sign language (USL) and test it through a machine learning classifier. The overview of the proposed system is divided into four main stages i.e., data collection, data acquisition, training model ad testing model. The USL dataset which is comprised of 1,560 images was created by photographing various hand positions using a camera. This work provides a strategy for automated identification of USL numbers based on a bag-of-words (BoW) paradigm. For classification purposes, support vector machine (SVM), Random Forest, and K-nearest neighbor (K-NN) are used with the BoW histogram bin frequencies as characteristics. The proposed technique outperforms others in number classification, attaining the accuracies of 88%, 90%, and 84% for the random forest, SVM, and K-NN respectively.
“…However, if the features are in the continuous form (that in our case is with the TF-IDF vectorization) then the Gaussian distribution will be employed [81]- [83], which modifies the function as per following:…”
Section: ) Classifiersmentioning
confidence: 99%
“…The evaluation of each experiment follows the statistical measures outlined in table 13 [83]. As the dataset is balanced for sentiment but somewhat imbalanced for sarcasm, we have used weighted statistics to measure the performance, rather than conventional statistics.…”
Humans have a natural tendency to express their emotions, but they are also skilled at using sarcasm to shape their feelings. In cognitive computing and natural language processing research, sentiment analysis and sarcasm detection are typically treated as separate tasks, with each text analyzed in isolation. However, this approach overlooks the connection between sentiment and sarcasm. We believe that sentiment and sarcasm are closely related and should be analyzed together to achieve a better understanding of context and natural language. In this paper, we propose a new framework that leverages the Cognitive Relationship (CR) between sarcasm and sentiment to improve the accuracy of classification. By taking into account the relationship between these two factors, we can achieve better results in sentiment analysis and sarcasm detection. We have also created a new and nearly balanced dataset for sentiment and sarcasm classification in standard Urdu that contains 7,000 tweets, which make up over 210K tokens. To gain a better understanding of the data, we conducted exploratory data analysis on words, hashtags, and emojis. The proposed methodology conducted a variety of classical machine learning classifiers and tested them with different variations of the dataset. After a thorough analysis of the results and errors, we found that the CR-based approach for sarcasm and sentiment classification performed better than the traditional stand-alone (SA) approach. Among the classifiers, Linear Regression and eXtreme Gradient Boosting proved to be the most effective. The sentiment classification based on CR has demonstrated a 9.3% enhancement compared to the stand-alone (SA) method while maintaining an overall improvement of approximately 22% compared to the baseline distribution. In the same way, the sarcasm classification based on CR has shown a 9.1% improvement over the SA approach and approximately 23.6% improvement over the baseline distribution.
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