chanakauomit Goto Github PK

aabc

admin-user

aladinmaster_al

albums

Using React-native Redux

authapp

bhoozt

book_list_js_oop

burger_app

car-project-with-react-redux

chanaka.info

chanakauomit

Portfolio

channelling

code-examples

A collection of code examples from blog posts etc.

codemommy

concept

Free Bootstrap 4 admin dashboard template

contactmanagerapp_react

daily-news-with-react-redux

design-patterns-core

designpatterns

This repository include the Java implementation of the Design Patterns

devportfolio

A lightweight, customizable single-page personal portfolio website template built with JavaScript and Sass

dialog-act-tagging-for-code-mixed-data-set

In a task oriented domain, recognizing the intention of a speaker is important so that the conversation can proceed in the correct direction. This is possible only if there is a way to label the utterance with its proper intent. One such labeling technique is Dialog Act (DA) tagging. The main goal of this thesis is to build a Dialog Act tagger for the Telugu English Code Mixed corpus. Dialogue Act (DA) classification plays a key role in dialogue interpretation, especially in spontaneous conversation analysis. Dialogue acts are defined as the meaning of each utterance at the illocutionary force level. Code-Mixing (CM) is a very commonly observed mode of communication in a multilingual configuration. The trends of using this newly emerging language have its effect as a culling option especially in platforms like social media. This becomes particularly important in the context of technology and health, where expressing the upcoming advancements is difficult in native language. Despite the change of such language dynamics, current dialog systems cannot handle a switch between languages across sentences and mixing within a sentence. Everyday conversations are fabricated in this mixed language and analyzing dialog acts in this language is very essential in further advancements of making interaction with personal assistants more natural. Almost all standard traditional supervised machine learning approaches to classification have been applied in DA classification, from Support Vector Machines (SVM), Naïve Bayes, NLTK Classifiers, Max Entropy Classifier, Multilayer Perceptron, Conditional Random Field Classifier and Hidden Markov Model (HMM).

ecommerce-react

eCommerce web application using React, Redux, Redux-Saga, Firebase and SASS.

ems

ems-backend

Education Managment System Laravel

ems-v1

ems_interim

ems_mobile

ems_v1

ems_v1.0

examplemate