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“Great work.”Manjinder S. 2 months ago
Graduate Research AssistantJan 2018 - Jan 2019 (1 year)
Worked under the Datasearch Center at University of Moratuwa as Graduate Research Assistant. At there I was able complete a research to estimate cultivate paddy extent and the extent affected by Brown Planthopper attacks in Sri Lanka. A novel approach has been introduced for remote sensing analysis using deep neural network technologies, particularly suitable for small scale crop lands in developing countries.
Software InternJul 2016 - Dec 2016 (5 months)
Worked as a software intern. I was exposed to latest technologies at that period such as React, NojeJS, Angular, MongoDB etc. I was able to gain knowledge about iOS and Android development as well.
BSc. Engineering2014 - 2017 (3 years)
MSc. specialized in Machine Learning2018 - 2019 (1 year)
Word Vector Embeddings and Domain Specific Semantic based Semi-Supervised Ontology Population
An ontology defines a set of representational primitives which model a domain of knowledge or discourse. The semantic sensitive word embedding has become a popular topic in natural language processing with its capability to cope with the semantic challenges. Thus, in this study we propose a novel way of semi-supervised ontology population through word embeddings and domain specific semantic similarity as the basis.
Legal Document Retrieval Using Document Vector Embeddings and Deep Learning
Domain specific information retrieval process has been a prominent and ongoing research in the field of natural language processing. The main bottleneck in these studies is the heavy coupling of domain experts, that makes the entire process to be time consuming and cumbersome. In this study, we have developed three novel models which are compared against a golden standard generated via the on line repositories provided, specifically for the legal domain.
Deriving a representative vector for ontology classes with instance word vector embeddings
Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks. This study proposes a methodology to derive a representative vector for ontology classes whose instances were converted to the vector space. We show that our methodology out-performs the traditional mean and median vector representations.
Semi-supervised instance population of an ontology using word vector embedding
In this study we propose a novel way of semi-supervised ontology population through word embeddings as the basis. We built several models including traditional benchmark models and new types of models which are based on word embeddings. Finally, we ensemble them together to come up with a synergistic model with better accuracy. We demonstrate that our ensemble model can outperform the individual models.
Synergistic union of Word2Vec and lexicon for domain specific semantic similarity
We introduce a domain specific semantic similarity measure that was created by the synergistic union of word2vec, a word embedding method that is used for semantic similarity calculation and lexicon based (lexical) semantic similarity methods. We prove that this proposed methodology outperforms both, word embedding methods trained on a generic corpus and word embedding methods trained on a domain specific corpus, which do not use lexical semantic similarity methods to augment the results.
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