Decision Tree based Supervised Word Sense Disambiguation for Assamese

Sarmah, Jumi; Sarma, Shikhar Kumar

doi:10.5120/ijca2016909488

Cited by 13 publications

(7 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DT has a flow chart-like structure. 23 It collects training data that have similar characteristics to the same area. Prediction is made based on the label that has the highest mode.…”

Section: Machine Learning Model Developmentmentioning

confidence: 99%

Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

et al. 2022

View full text Add to dashboard Cite

Nutrient solution plays an essential role in providing macronutrients to hydroponic plants. Determining nitrogen in the form of nitrate is crucial, as either a deficient or excessive supply of nitrate ions may reduce the plant yield or lead to environmental pollution. This work aims to evaluate the performance of feature reduction techniques and conventional machine learning (ML) algorithms in determining nitrate concentration levels. Two features reduction techniques [Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA)] and seven ML algorithms [k-Nearest Neighbors, Support Vector Machine, Decision Trees, Naïve Bayes, Random Forest (RF), Gradient Boosting, and Extreme Gradient Boosting] were evaluated using a high-dimensional spectroscopic dataset containing measured nitrate-nitrite mixed solution absorbance data. Despite the limited and uneven number of samples per class, this study demonstrated that PCA outperformed LDA on the high-dimensional spectroscopic dataset. The classification accuracy of ML algorithms combined with PCA ranged from 92.7 % to 99.8 %, whereas the classification accuracy of ML algorithms combined with LDA ranged from 80.7 % to 87.6 %. The PCA with the RF algorithm exhibited the best performance with 99.8% accuracy.

show abstract

“…DT has a flow chart-like structure. 23 It collects training data that have similar characteristics to the same area. Prediction is made based on the label that has the highest mode.…”

Section: Machine Learning Model Developmentmentioning

confidence: 99%

Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The DT [41][42][43]-based approach frames the rules in the form of a tree structure (figure 1) where the non-leaf nodes denote the tests and the branches represent the test results. The leaf nodes of the tree carry the different senses.…”

Section: Dtmentioning

confidence: 99%

A novel approach to word sense disambiguation in Bengali language using supervised methodology

et al. 2019

View full text Add to dashboard Cite

An attempt is made in this paper to report how a supervised methodology has been adopted for the task of Word Sense Disambiguation (WSD) in Bengali with necessary modifications. At the initial stage, four commonly used supervised methods, Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Naïve Bayes (NB), are developed at the baseline. These algorithms are applied individually on a data set of 13 most frequently used Bengali ambiguous words. On experimental basis, the baseline strategy is modified with two extensions: (a) inclusion of lemmatization process into the system and (b) bootstrapping of the operational process. As a result, the levels of accuracy of the baseline methods are slightly improved, which is a positive signal for the whole process of disambiguation as it opens scope for further modification of the existing method for better result. In this experiment, the data sets are prepared from the Bengali corpus, developed in the Technology Development for Indian Languages (TDIL) project of the Government of India and from the Bengali WordNet, which is developed at the Indian Statistical Institute, Kolkata. The paper reports the challenges and pitfalls of the work that have been closely observed during the experiment.

show abstract

“…Assamese: Sarmah and Sarma [16] have proposed a supervised WSD system based on decision tree. The system consists of four modules: (a) preprocessing of raw data, (b) sense inventory preparation, (c) feature selection and (d) constructing the decision tree.…”

Section: 2dmentioning

confidence: 99%

Word Sense Disambiguation in Bengali language using unsupervised methodology with modifications

Pal

Saha

2019

Sādhanā

View full text Add to dashboard Cite

In this work, Word Sense Disambiguation (WSD) in Bengali language is implemented using unsupervised methodology. In the first phase of this experiment, sentence clustering is performed using Maximum Entropy method and the clusters are labelled with their innate senses by manual intervention, as these sense-tagged clusters could be used as sense inventories for further experiment. In the next phase, when a test data comes to be disambiguated, the Cosine Similarity Measure is used to find the closeness of that test data with the initially sense-tagged clusters. The minimum distance of that test data from a particular sense-tagged cluster assigns the same sense to the test data as that of the cluster it is assigned with. This strategy is considered as the baseline strategy, which produces 35% accurate result in WSD task. Next, two extensions are adopted over this baseline strategy: (a) Principal Component Analysis (PCA) over the feature vector, which produces 52% accuracy in WSD task and (b) Context Expansion of the sentences using Bengali WordNet coupled with PCA, which produces 61% accuracy in WSD task. The data sets that are used in this work are obtained from the Bengali corpus, developed under the Technology Development for the Indian Languages (TDIL) project of the Government of India, and the lexical knowledge base (i.e., the Bengali WordNet) used in the work is developed at the Indian Statistical Institute, Kolkata, under the Indradhanush Project of the DeitY, Government of India. The challenges and the pitfalls of this work are also described in detail in the pre-conclusion section.

show abstract

Decision Tree based Supervised Word Sense Disambiguation for Assamese

Cited by 13 publications

References 7 publications

Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

Nitrate Classification Based on Optical Absorbance Data Using Machine Learning Algorithms for a Hydroponics System

A novel approach to word sense disambiguation in Bengali language using supervised methodology

Word Sense Disambiguation in Bengali language using unsupervised methodology with modifications

Contact Info

Product

Resources

About