scholarly journals Using Artificial Intelligence in Source Code Summarization: A Review

2021 ◽  
Author(s):  
Shraddha Birari ◽  
Sukhada Bhingarkar
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Software Maintenance ◽  
Source Code ◽  
Machine Intelligence ◽  
Manual Intervention ◽  
Research Challenges ◽  
Wide Range

Source code summarization is the methodology of generating the description from the source code. The summary of the source code gives the brief idea of the functionality performed by the source code. Summary of the code is always necessary for software maintenance. Summaries are not only beneficial for software maintenance but also for code categorization and retrieval. Generation of summary in an automated fashion instead of manual intervention can save the time and efforts. Artificial Intelligence is a very popular branch in the field of computer science that demonstrates machine intelligence and covers a wide range of applications. This paper focuses on the use of Artificial Intelligence for source code summarization. Natural Language Processing (NLP) and Machine Learning (ML) are considered to be the subsets of Artificial Intelligence. Thus, this paper presents a critical review of various NLP and ML techniques implemented so far for generating summaries from the source code and points out research challenges in this field.

scholarly journals What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning

2019 ◽  
Vol 33 ◽  
pp. 6674-6681 ◽  
Author(s):  
Irene Li ◽  
Alexander R. Fabbri ◽  
Robert R. Tung ◽  
Dragomir R. Radev
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Online Courses ◽  
Specific Topic ◽  
Reading List ◽  
University Courses

Recent years have witnessed the rising popularity of Natural Language Processing (NLP) and related fields such as Artificial Intelligence (AI) and Machine Learning (ML). Many online courses and resources are available even for those without a strong background in the field. Often the student is curious about a specific topic but does not quite know where to begin studying. To answer the question of “what should one learn first,”we apply an embedding-based method to learn prerequisite relations for course concepts in the domain of NLP. We introduce LectureBank, a dataset containing 1,352 English lecture files collected from university courses which are each classified according to an existing taxonomy as well as 208 manually-labeled prerequisite relation topics, which is publicly available 1. The dataset will be useful for educational purposes such as lecture preparation and organization as well as applications such as reading list generation. Additionally, we experiment with neural graph-based networks and non-neural classifiers to learn these prerequisite relations from our dataset.

scholarly journals Reports of the Workshops Held at the Tenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 99-102
Author(s):  
Tiffany Barnes ◽  
Oliver Bown ◽  
Michael Buro ◽  
Michael Cook ◽  
Arne Eigenfeldt ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
North Carolina ◽  
Natural Language Processing ◽  
Natural Language ◽  
Real Time ◽  
Language Processing ◽  
State University ◽  
North Carolina State University ◽  
Wide Range ◽  
Digital Entertainment

The AIIDE-14 Workshop program was held Friday and Saturday, October 3–4, 2014 at North Carolina State University in Raleigh, North Carolina. The workshop program included five workshops covering a wide range of topics. The titles of the workshops held Friday were Games and Natural Language Processing, and Artificial Intelligence in Adversarial Real-Time Games. The titles of the workshops held Saturday were Diversity in Games Research, Experimental Artificial Intelligence in Games, and Musical Metacreation. This article presents short summaries of those events.

Artificial Intelligence and Investing

2011 ◽  
pp. 237-240 ◽  
Author(s):  
Roy Rada
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Risk Management ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Asset Valuation ◽  
Stock Valuation ◽  
Knowledge Based ◽  
Processing Techniques

The techniques of artificial intelligence include knowledgebased, machine learning, and natural language processing techniques. The discipline of investing requires data identification, asset valuation, and risk management. Artificial intelligence techniques apply to many aspects of financial investing, and published work has shown an emphasis on the application of knowledge-based techniques for credit risk assessment and machine learning techniques for stock valuation. However, in the future, knowledge-based, machine learning, and natural language processing techniques will be integrated into systems that simultaneously address data identification, asset valuation, and risk management.

Applications of Artificial Intelligence in the Realm of Business Intelligence

2021 ◽  
pp. 358-386
Author(s):  
Prakhar Mehrotra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Time Series ◽  
Natural Language Processing ◽  
Language Processing ◽  
Business Intelligence ◽  
Machine Learning Techniques ◽  
Current State ◽  
Learning Techniques ◽  
High Level

The objective of this chapter is to discuss the integration of advancements made in the field of artificial intelligence into the existing business intelligence tools. Specifically, it discusses how the business intelligence tool can integrate time series analysis, supervised and unsupervised machine learning techniques and natural language processing in it and unlock deeper insights, make predictions, and execute strategic business action from within the tool itself. This chapter also provides a high-level overview of current state of the art AI techniques and provides examples in the realm of business intelligence. The eventual goal of this chapter is to leave readers thinking about what the future of business intelligence would look like and how enterprise can benefit by integrating AI in it.

scholarly journals Essential Features in a Theory of Context for Enabling Artificial General Intelligence

2021 ◽  
Vol 11 (24) ◽  
pp. 11991
Author(s):  
Mayank Kejriwal
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Natural Language ◽  
Language Processing ◽  
Representation Learning ◽  
Machine Intelligence ◽  
General Intelligence ◽  
Commonsense Reasoning ◽  
Core Concept

Despite recent Artificial Intelligence (AI) advances in narrow task areas such as face recognition and natural language processing, the emergence of general machine intelligence continues to be elusive. Such an AI must overcome several challenges, one of which is the ability to be aware of, and appropriately handle, context. In this article, we argue that context needs to be rigorously treated as a first-class citizen in AI research and discourse for achieving true general machine intelligence. Unfortunately, context is only loosely defined, if at all, within AI research. This article aims to synthesize the myriad pragmatic ways in which context has been used, or implicitly assumed, as a core concept in multiple AI sub-areas, such as representation learning and commonsense reasoning. While not all definitions are equivalent, we systematically identify a set of seven features associated with context in these sub-areas. We argue that such features are necessary for a sufficiently rich theory of context, as applicable to practical domains and applications in AI.

scholarly journals Competitor Identification by Use The Sentiment Classification Based on the User Research

2019 ◽  
Vol 8 (9S2) ◽  
pp. 871-874
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
The Other ◽  
Machine Learning Techniques ◽  
Single Product ◽  
User Research ◽  
Customer Reviews ◽  
Learning Techniques ◽  
Wide Range

Online shopping's have achieved an immense growth. All like to do it as there is no need to physically to the shop and we have a wide range of collections available in the online sites from which we can actually buy the product. The customers usually tend to purchase a product that has a good customer review and has the highest rating. Numerous reviews are given for a single product and the most of the important reviews are not organized well which makes it disappear from the other reviews. Numerous researchers have worked on structuring the reviews for various purposes. In this work we propose a sentimental analysis of customer reviews for various hotel items. All the items are reviewed by the customers and the proposed work makes an analysis of the reviews obtained for a particular item in all the available shops. This analysis is helpful injudging the most likely consumed food by the customers around and can get to know the competiveness of the product being delivered to the customers. Machine Learning techniques and Natural language Processing (NLP) are used for the proposed work and is observed to produce an efficient result.

scholarly journals An Analysis of the Applications of Natural Language Processing in Various Sectors

2021 ◽  
Author(s):  
Priya B ◽  
Nandhini J.M ◽  
Gnanasekaran T
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Health Sector ◽  
Automated System ◽  
Human Language ◽  
Agriculture Sector ◽  
Prediction Of Diabetes

Natural Language processing (NLP) dealing with Artificial Intelligence concept is a subfield of Computer Science, enabling computers to understand and process human language. Natural Language Processing being a part of artificial intelligence provides understanding of human language by computers for the purpose of extracting information or insights and create meaningful response. It involves creating algorithms that transform text in to words labeling With the emerging advancements in Machine learning and Deep Learning, NLP can contributed a lot towards health sector, education, agriculture and so on. This paper summarizes the various aspects of NLP along with case studies associated with Health Sector for Voice Automated System, prediction of Diabetes Millets, Crop Detection technique in Agriculture Sector.

scholarly journals Depression Therapy Using Chatbot

2020 ◽  
Vol 6 (12) ◽  
pp. 323-327
Author(s):  
Arkodeep Biswas and Ajay Kaushik
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Operating System ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Application ◽  
Text Chat ◽  
Communication History ◽  
Chat Bot

The objective of this paper is to build a Web Application based on Virtual voice and chat Assistant. The current study focuses on development of voice and text/chat bot specifically. It is specially being built for people who feel depressed and insists them to talk open mindedly which in turn pacifies them. As the name of the application suggests, App: An application to pacify people and make them as happy as a cat would be with his or her mother (the reason why a cat purrs). We will be using Dialog flow for the application design and Machine Learning as a part of Artificial Intelligence for Natural Language Processing (NLP), an easiest way to use Machine Learning libraries. At the back-end we will be using a database to store the communication history between the user and the bot. This application will only work on devices with Web operating system version-5.0 and above.

scholarly journals Disease Prediction Chatbot

2021 ◽  
pp. 632-636
Author(s):  
Shraddha A. S ◽  
Shreepada Bhat ◽  
Shubhashri V. K ◽  
Sinchana Karnik ◽  
Narender M
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Learning Algorithm ◽  
Recent Decade ◽  
Standard Of Living ◽  
Disease Prediction ◽  
Machine Learning Algorithm

Applications in the field of machine learning and artificial intelligence have been in great demand over the recent decade. Now it has various applications in the field of health industry. With the help of machine learning algorithm prediction of diseases has been made easier. Now doctors can concentrate only on treatment with the help of technology. Technology is accelerating innovations in the healthcare domain which has increased people’s standard of living over the years. Here in our project we are making a healthcare chatbot with help of Natural language processing and machine learning algorithm to predict disease. User interacts with the chatbot just like one interacts with his doctor and based on the symptoms provided by users and the chatbot will identify the symptom and predict the disease.

Sign in / Sign up

Export Citation Format

Share Document