College Information Chat-Bot System Based on Natural Language Processing

2020 ◽  
Vol 14 (5) ◽  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
College Information ◽  
Chat Bot

scholarly journals Natural Language Processing in OTF Computing: Challenges and the Need for Interactive Approaches

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 22
Author(s):  
Frederik Bäumer ◽  
Joschka Kersting ◽  
Michaela Geierhos
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Ad Hoc ◽  
Domain Specific ◽  
Compensation Process ◽  
Language Requirement ◽  
Chat Bot ◽  
The Given ◽  
Software Services

The vision of On-the-Fly (OTF) Computing is to compose and provide software services ad hoc, based on requirement descriptions in natural language. Since non-technical users write their software requirements themselves and in unrestricted natural language, deficits occur such as inaccuracy and incompleteness. These deficits are usually met by natural language processing methods, which have to face special challenges in OTF Computing because maximum automation is the goal. In this paper, we present current automatic approaches for solving inaccuracies and incompletenesses in natural language requirement descriptions and elaborate open challenges. In particular, we will discuss the necessity of domain-specific resources and show why, despite far-reaching automation, an intelligent and guided integration of end users into the compensation process is required. In this context, we present our idea of a chat bot that integrates users into the compensation process depending on the given circumstances.

scholarly journals Pengembangan aplikasi chat bot guna membantu perusahaan dalam mengedukasi pelanggan dengan sistem natural language processing

TEKNO ◽  
2019 ◽  
Vol 29 (2) ◽  
pp. 129
Author(s):  
Yohanes Dhimas Firman Syahputra ◽  
Syaad Patmanthara ◽  
Heru Wahyu Herwanto
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Data Set ◽  
Chat Bot

Hasil pengembangan aplikasi kecerdasan buatan berupa chat bot guna membantu perusahaan dalam melakukan edukasi customer dengan sistem natural language processing (NLP) diperoleh melalui metode pengembangan sistem. Dimana chat bot guna membantu perusahaan dalam melakukan edukasi customer dengan sistem NLP ini dikembangkan untuk komputer dapat melakukan tugas tertentu seperti yang dilakukan oleh manusia seperti robot chatting (chatbot), yaitu sistem yang mengadopsi pengetahuan manusia ke komputer, agar komputer dapat melakukan percakapan dengan pengguna. Kepintaran chatbot dalam menjawab pertanyaan ditentukan oleh banyaknya data set sehingga perbanyak data jawaban agar lebih banyak memahami pertanyaan dari pelanggan. Berdasarkan hasil uji coba pengembangan Chatbot yang telah dilakuan skor yang diproleh adalah 88,94%. Berdasarkan tabel kategori kelayakan, maka chat bot yang dikembangkan dalam penelitian dapat dinyatakan “sangat layak” untuk digunakan dalam pengembangannya.

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 AI Based Student Bot for Academic Information System using Machine Learning

2019 ◽  
pp. 590-596
Author(s):  
Vijayakumar R ◽  
Bhuvaneshwari B ◽  
Adith S ◽  
Deepika M
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Python Language ◽  
Time Period ◽  
Long Time ◽  
Academic Information ◽  
Chat Bot

In General all the institutions like colleges sends their notes and information to students individually. Sometimes the student can�t access it quickly and repetition of data also increased. The realm of this work is to create a Chatbot for the college purpose. Our work reduces the human work to send every details and notes to all departments by email or some other medium. In this work, academic information's /details feed it to the database which will be available for the long time period. The academic information consists of information about placements details, exam time tables, semester notes and upcoming events. A Chatbot is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. The chat bot stores the data by key words and when the user entered data is matched with the key it reply the assigned data for it. The Chatbot is created by using python language and Natural language processing. This project make use of the MySQL database to store the information. With the help of natural language processing the bot AI understand the message sent by the user and reply with the matched key value. In this Chatbot the user first need to login by their college roll number and Department. When the valid person asks about the particular information by text the information gets retrieved from the updated database that related to their department. Through this chat box the student can easily access whenever they want and the data need not to be update more than once.

scholarly journals Acronym Disambiguation using Web Scraping

2020 ◽  
Vol 9 (4) ◽  
pp. 256-260
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Named Entity Recognition ◽  
Similarity Score ◽  
Cosine Similarity ◽  
Entity Recognition ◽  
Named Entity ◽  
Web Scraping ◽  
Chat Bot

Web Scraping is one of the current technologies that uses scraping tools to perform tasks similar to humans. It is adopted in many applications like e-commerce, dataset creating in machine learning, advertising etc. This work focuses on acronym disambiguation which is part of natural language processing. Acronym disambiguation is mainly used in chat bot, named entity recognition, natural language processing and so on. In this paper, an acronym disambiguation system is built by web scraping using Jsoup and cosine similarity score is used to identify the most suitable acronym. Our goal is to identify the acronym suitable for the abbreviation based on context of the paragraph where it lies. For this we use cosine similarity to calculate the score, the acronym which obtains maximum score is the concluded as suitable expansion

A Review and evaluation of Machine Translation methods for Lumasaaba

2020 ◽  
pp. 3-17
Author(s):  
Peter Nabende
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Machine Translation ◽  
Language Processing ◽  
Research Area ◽  
Data Driven ◽  
East African ◽  
Data Set ◽  
African Languages ◽  
Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

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