A Customisable Chatbot As a Research Instrument
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Intelligent Chat Bot
INTELLIGENT CHAT BOT A. Mohamed Rasvi, V.V. Sabareesh, V. Suthajebakumari Computer Science and Engineering, Kamaraj College of Engineering and Technology, India ABSTRACT This paper discusses the workflow of intelligent chat bot powered by various artificial intelligence algorithms. The replies for messages in chats are trained against set of predefined questions and chat messages. These trained data sets are stored in database. Relying on one machine-learning algorithm showed inaccurate performance, so this bot is powered by four different machine-learning algorithms to make a decision. The inference engine pre-processes the received message then matches it against the trained datasets based on the AI algorithms. The AIML provides similar way of replying to a message in online chat bots using simple XML based mechanism but the method of employing AI provides accurate replies than the widely used AIML in the Internet. This Intelligent chat bot can be used to provide assistance for individual like answering simple queries to booking a ticket for a trip and also when trained properly this can be used as a replacement for a teacher which teaches a subject or even to teach programming. Keywords : AIML, Artificial Intelligence, Chat bot, Machine-learning, String Matching. I. INTRODUCTION Social networks are attracting masses and gaining huge momentum. They allow instant messaging and sharing features. Guides and technical help desks provide on demand tech support through chat services or voice call. Queries are taken to technical support team from customer to clear their doubts. But this process needs a dedicated support team to answer user‟s query which is a lot of man power. -
An Ensemble Regression Approach for Ocr Error Correction
AN ENSEMBLE REGRESSION APPROACH FOR OCR ERROR CORRECTION by Jie Mei Submitted in partial fulfillment of the requirements for the degree of Master of Computer Science at Dalhousie University Halifax, Nova Scotia March 2017 © Copyright by Jie Mei, 2017 Table of Contents List of Tables ................................... iv List of Figures .................................. v Abstract ...................................... vi List of Symbols Used .............................. vii Acknowledgements ............................... viii Chapter 1 Introduction .......................... 1 1.1 Problem Statement............................ 1 1.2 Proposed Model .............................. 2 1.3 Contributions ............................... 2 1.4 Outline ................................... 3 Chapter 2 Background ........................... 5 2.1 OCR Procedure .............................. 5 2.2 OCR-Error Characteristics ........................ 6 2.3 Modern Post-Processing Models ..................... 7 Chapter 3 Compositional Correction Frameworks .......... 9 3.1 Noisy Channel ............................... 11 3.1.1 Error Correction Models ..................... 12 3.1.2 Correction Inferences ....................... 13 3.2 Confidence Analysis ............................ 16 3.2.1 Error Correction Models ..................... 16 3.2.2 Correction Inferences ....................... 17 3.3 Framework Comparison ......................... 18 ii Chapter 4 Proposed Model ........................ 21 4.1 Error Detection .............................. 22 -
Mobile + Cloud Apps What Does Hawaii Offer?
cloud in the palm of your hands Victor Bahl 7.28.2011 mobile phone market IDC FY12 forecast 518 million SmartPhones sold world-wide • More smartphones shipped than PCs in FY11 Q2 (101M vs. 92M) WW Mobile Phone Device Shipments Billions 1.8 1.7 1.6 1.6 1.5 1.4 40% 1.4 1.3 37% 1.2 1.2 33% 29% 1.0 26% 0.8 24% 0.6 0.4 0.2 0.0 2009 2010 2011 2012 2013 2014 Other Mobile Phones Smartphones Source: IDC, iSuppli, Gartner, Accenture analysis. sad reality of mobile computing hardware limitations . vs. static elements of same era (desktops, servers) . weight, power, size constraints . CPU, memory, display, keyboard wireless communication uncertainty . bandwidth / latency variation . intermittent connectivity . may cost real money, require service agreements finite energy source . actions may be slowed or deferred . wireless communication costs energy why resource poverty hurts . No “Moore’s Law” for human attention . Being mobile consumes human attention . Already scarce resource is further taxed by resource poverty Human Attention Human Adam & Eve 2000 AD Reduce demand on human attention • Software computing demands not rigidly constrained • Many “expensive” techniques become a lot more useable when mobile Some examples • machine learning, activity inferencing, context awareness • natural language translation, speech recognition, Vastly superior mobile • computer vision, context awareness, augmented reality user experience • reuse of familiar (non-mobile) software environments Clever exploitation needed to deliver these benefits Courtesy. M. Satya, CMU battery trends Li-Ion Energy Density • Lagged behind o Higher voltage batteries (4.35 250 V vs. 4.2V) – 8% improvement o Silicon anode adoption (vs. -
Artificial Intelligence, Big Data and Cloud Computing 144
Digital Business and Electronic Digital Business Models StrategyCommerceProcess Instruments Strategy, Business Models and Technology Lecture Material Lecture Material Prof. Dr. Bernd W. Wirtz Chair for Information & Communication Management German University of Administrative Sciences Speyer Freiherr-vom-Stein-Straße 2 DE - 67346 Speyer- Email: [email protected] Prof. Dr. Bernd W. Wirtz Chair for Information & Communication Management German University of Administrative Sciences Speyer Freiherr-vom-Stein-Straße 2 DE - 67346 Speyer- Email: [email protected] © Bernd W. Wirtz | Digital Business and Electronic Commerce | May 2021 – Page 1 Table of Contents I Page Part I - Introduction 4 Chapter 1: Foundations of Digital Business 5 Chapter 2: Mobile Business 29 Chapter 3: Social Media Business 46 Chapter 4: Digital Government 68 Part II – Technology, Digital Markets and Digital Business Models 96 Chapter 5: Digital Business Technology and Regulation 97 Chapter 6: Internet of Things 127 Chapter 7: Artificial Intelligence, Big Data and Cloud Computing 144 Chapter 8: Digital Platforms, Sharing Economy and Crowd Strategies 170 Chapter 9: Digital Ecosystem, Disintermediation and Disruption 184 Chapter 10: Digital B2C Business Models 197 © Bernd W. Wirtz | Digital Business and Electronic Commerce | May 2021 – Page 2 Table of Contents II Page Chapter 11: Digital B2B Business Models 224 Part III – Digital Strategy, Digital Organization and E-commerce 239 Chapter 12: Digital Business Strategy 241 Chapter 13: Digital Transformation and Digital Organization 277 Chapter 14: Digital Marketing and Electronic Commerce 296 Chapter 15: Digital Procurement 342 Chapter 16: Digital Business Implementation 368 Part IV – Digital Case Studies 376 Chapter 17: Google/Alphabet Case Study 377 Chapter 18: Selected Digital Case Studies 392 Chapter 19: The Digital Future: A Brief Outlook 405 © Bernd W. -
Practice with Python
CSI4108-01 ARTIFICIAL INTELLIGENCE 1 Word Embedding / Text Processing Practice with Python 2018. 5. 11. Lee, Gyeongbok Practice with Python 2 Contents • Word Embedding – Libraries: gensim, fastText – Embedding alignment (with two languages) • Text/Language Processing – POS Tagging with NLTK/koNLPy – Text similarity (jellyfish) Practice with Python 3 Gensim • Open-source vector space modeling and topic modeling toolkit implemented in Python – designed to handle large text collections, using data streaming and efficient incremental algorithms – Usually used to make word vector from corpus • Tutorial is available here: – https://github.com/RaRe-Technologies/gensim/blob/develop/tutorials.md#tutorials – https://rare-technologies.com/word2vec-tutorial/ • Install – pip install gensim Practice with Python 4 Gensim for Word Embedding • Logging • Input Data: list of word’s list – Example: I have a car , I like the cat → – For list of the sentences, you can make this by: Practice with Python 5 Gensim for Word Embedding • If your data is already preprocessed… – One sentence per line, separated by whitespace → LineSentence (just load the file) – Try with this: • http://an.yonsei.ac.kr/corpus/example_corpus.txt From https://radimrehurek.com/gensim/models/word2vec.html Practice with Python 6 Gensim for Word Embedding • If the input is in multiple files or file size is large: – Use custom iterator and yield From https://rare-technologies.com/word2vec-tutorial/ Practice with Python 7 Gensim for Word Embedding • gensim.models.Word2Vec Parameters – min_count: -
NLP - Assignment 2
NLP - Assignment 2 Week 2 December 27th, 2016 1. A 5-gram model is a order Markov Model: (a) Six (b) Five (c) Four (d) Constant Ans : c) Four 2. For the following corpus C1 of 3 sentences, what is the total count of unique bi- grams for which the likelihood will be estimated? Assume we do not perform any pre-processing, and we are using the corpus as given. (i) ice cream tastes better than any other food (ii) ice cream is generally served after the meal (iii) many of us have happy childhood memories linked to ice cream (a) 22 (b) 27 (c) 30 (d) 34 Ans : b) 27 3. Arrange the words \curry, oil and tea" in descending order, based on the frequency of their occurrence in the Google Books n-grams. The Google Books n-gram viewer is available at https://books.google.com/ngrams: (a) tea, oil, curry (c) curry, tea, oil (b) curry, oil, tea (d) oil, tea, curry Ans: d) oil, tea, curry 4. Given a corpus C2, The Maximum Likelihood Estimation (MLE) for the bigram \ice cream" is 0.4 and the count of occurrence of the word \ice" is 310. The likelihood of \ice cream" after applying add-one smoothing is 0:025, for the same corpus C2. What is the vocabulary size of C2: 1 (a) 4390 (b) 4690 (c) 5270 (d) 5550 Ans: b)4690 The Questions from 5 to 10 require you to analyse the data given in the corpus C3, using a programming language of your choice. -
3 Dictionaries and Tolerant Retrieval
Online edition (c)2009 Cambridge UP DRAFT! © April 1, 2009 Cambridge University Press. Feedback welcome. 49 Dictionaries and tolerant 3 retrieval In Chapters 1 and 2 we developed the ideas underlying inverted indexes for handling Boolean and proximity queries. Here, we develop techniques that are robust to typographical errors in the query, as well as alternative spellings. In Section 3.1 we develop data structures that help the search for terms in the vocabulary in an inverted index. In Section 3.2 we study WILDCARD QUERY the idea of a wildcard query: a query such as *a*e*i*o*u*, which seeks doc- uments containing any term that includes all the five vowels in sequence. The * symbol indicates any (possibly empty) string of characters. Users pose such queries to a search engine when they are uncertain about how to spell a query term, or seek documents containing variants of a query term; for in- stance, the query automat* would seek documents containing any of the terms automatic, automation and automated. We then turn to other forms of imprecisely posed queries, focusing on spelling errors in Section 3.3. Users make spelling errors either by accident, or because the term they are searching for (e.g., Herman) has no unambiguous spelling in the collection. We detail a number of techniques for correcting spelling errors in queries, one term at a time as well as for an entire string of query terms. Finally, in Section 3.4 we study a method for seeking vo- cabulary terms that are phonetically close to the query term(s). -
US 'To Cut Immigrant Detention'
Internet News Record 06/10/09 - 06/10/09 LibertyNewsprint.com U.S. Edition The First Draft: David Letterman and US 'to cut immigrant the Dalai Lama detention' By Deborah Zabarenko (Front Serious subjects, all of them. (BBC News | Americas | World violent crimes, the New York Row Washington) And what was the top story on the Edition) Times reported. Submitted at 10/6/2009 6:11:02 AM morning network newscasts? Ten Submitted at 10/6/2009 7:41:54 AM "Serious felons deserve to be in points if you guessed the natural the prison model," Ms Napolitano This is one of those Washington choice: David Letterman’s sex US officials are expected to told the newspaper. days that seems to defy a theme. life. announce plans that would allow "But there are others. There are Consider: What does this say about illegal immigrants not considered women. There are children." Iran is the topic at the Senate Washington? The U.S. media? a threat to be taken out of jails, Proposals for using alternatives Banking Committee, where The public appetite for scandal? reports say. to prison detention are expected to officials from the State and Let us know what you think. The new policy would list be submitted to Congress in the Treasury departments are set to Click here for more Reuters immigrants according to the risk coming weeks. testify on economic sanctions political coverage they may pose, the Wall Street The Wall Street Journal cited against Tehran. Photo credits: Journal reports. officials as saying the Afghanistan is expected to be REUTERS/Christinne Muschi Detainees who are not criminals administration would ask the front and center when President (exiled Tibetan spiritual leader the could be kept in hotels and private sector for ideas, including Barack Obama briefs Dalai Lama, Montreal, Canada, nursing homes, according to leaks for the construction of model congressional leaders about his October 3, 2009) of the plans. -
Understanding User Satisfaction with Intelligent Assistants
Understanding User Satisfaction with Intelligent Assistants Julia Kiseleva1;∗ Kyle Williams2;∗ Ahmed Hassan Awadallah3 Aidan C. Crook3 Imed Zitouni3 Tasos Anastasakos3 1Eindhoven University of Technology, [email protected] 2Pennsylvania State University, [email protected] 3Microsoft, {hassanam, aidan.crook, izitouni, tasos.anastasakos}@microsoft.com ABSTRACT Q1: how is the weather in Chicago User’s dialog Voice-controlled intelligent personal assistants, such as Cortana, Q2: how is it this weekend with Cortana: (A) Task is “Finding a Google Now, Siri and Alexa, are increasingly becoming a part of Q3: find me hotels in Chicago hotel in Chicago” users’ daily lives, especially on mobile devices. They allow for a Q4: which one of these is the cheapest radical change in information access, not only in voice control and Q5: which one of these has at least 4 stars touch gestures but also in longer sessions and dialogues preserving context, necessitating to evaluate their effectiveness at the task or Q6: find me direc>ons from the Chicago airport to number one session level. However, in order to understand which type of user User’s dialog Q1: find me a pharmacy nearby interactions reflect different degrees of user satisfaction we need with Cortana: (B) explicit judgements. In this paper, we describe a user study that Q2: which of these is highly rated Task is “Finding a pharmacy” was designed to measure user satisfaction over a range of typical Q3: show more informa>on about number 2 scenario’s of use: controlling a device, web search, and structured Q4: how long will it take me to get there search dialog. -
Use of Word Embedding to Generate Similar Words and Misspellings for Training Purpose in Chatbot Development
USE OF WORD EMBEDDING TO GENERATE SIMILAR WORDS AND MISSPELLINGS FOR TRAINING PURPOSE IN CHATBOT DEVELOPMENT by SANJAY THAPA THESIS Submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Science at The University of Texas at Arlington December, 2019 Arlington, Texas Supervising Committee: Deokgun Park, Supervising Professor Manfred Huber Vassilis Athitsos Copyright © by Sanjay Thapa 2019 ACKNOWLEDGEMENTS I would like to thank Dr. Deokgun Park for allowing me to work and conduct the research in the Human Data Interaction (HDI) Lab in the College of Engineering at the University of Texas at Arlington. Dr. Park's guidance on the procedure to solve problems using different approaches has helped me to grow personally and intellectually. I am also very thankful to Dr. Manfred Huber and Dr. Vassilis Athitsos for their constant guidance and support in my research. I would like to thank all the members of the HDI Lab for their generosity and company during my time in the lab. I also would like to thank Peace Ossom Williamson, the director of Research Data Services at the library of the University of Texas at Arlington (UTA) for giving me the opportunity to work as a Graduate Research Assistant (GRA) in the dataCAVE. i DEDICATION I would like to dedicate my thesis especially to my mom and dad who have always been very supportive of me with my educational and personal endeavors. Furthermore, my sister and my brother played an indispensable role to provide emotional and other supports during my graduate school and research. ii LIST OF ILLUSTRATIONS Fig: 2.3: Rasa Architecture ……………………………………………………………….7 Fig 2.4: Chatbot Conversation without misspelling and with misspelling error. -
Feature Combination for Measuring Sentence Similarity
FEATURE COMBINATION FOR MEASURING SENTENCE SIMILARITY Ehsan Shareghi Nojehdeh A thesis in The Department of Computer Science and Software Engineering Presented in Partial Fulfillment of the Requirements For the Degree of Master of Computer Science Concordia University Montreal,´ Quebec,´ Canada April 2013 c Ehsan Shareghi Nojehdeh, 2013 Concordia University School of Graduate Studies This is to certify that the thesis prepared By: Ehsan Shareghi Nojehdeh Entitled: Feature Combination for Measuring Sentence Similarity and submitted in partial fulfillment of the requirements for the degree of Master of Computer Science complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Signed by the final examining commitee: Chair Dr. Peter C. Rigby Examiner Dr. Leila Kosseim Examiner Dr. Adam Krzyzak Supervisor Dr. Sabine Bergler Approved Chair of Department or Graduate Program Director 20 Dr. Robin A. L. Drew, Dean Faculty of Engineering and Computer Science Abstract Feature Combination for Measuring Sentence Similarity Ehsan Shareghi Nojehdeh Sentence similarity is one of the core elements of Natural Language Processing (NLP) tasks such as Recognizing Textual Entailment, and Paraphrase Recognition. Over the years, different systems have been proposed to measure similarity between fragments of texts. In this research, we propose a new two phase supervised learning method which uses a combination of lexical features to train a model for predicting similarity between sentences. Each of these features, covers an aspect of the text on implicit or explicit level. The two phase method uses all combinations of the features in the feature space and trains separate models based on each combination. -
Keith Drummond (561) 8460337 [email protected] Drummond [email protected]
Keith Drummond (561) 8460337 www.KeithDrummond.com [email protected] [email protected] OBJECTIVE Result Driven Application Developer with end to end experience seeking a position as a Web, Data services developer. Specialties ● Programming C#, JAVASCRIPT, Reactjs, ANGULARJS, ASP.NET MVC, WebApi ● Database Management Microsoft SQL Server, MYSQL, NoSql ● Database Programming TSQL, SSIS, PLSQL ● OS Administration Windows 3.1 Windows 8.1, Windows Server 20002012 ● PC and Network Hardware New systems and upgrades / repairs ● Skillful Debugger/Trouble shooter ● Test Driven Development, and Unit Testing SUMMARY ● 7 Years’ experience in Software Development and QA Testing ● Comfortable in Agile development with scrum environment ● Strong knowledge of relational databases and IT Systems. ● In Depth hands on experienced with Server Management Studio 20052014. ● Expert Knowledge in upgrading custom software and defect management ● Worked as group member in all parts of the software development lifecycle including requirements, analysis, work breakdown, estimating, design, coding, bug fixes, and release management. EXPERIENCE DETAIL Senior Application Developer Consultant AAA Auto Club South September 2015 Present Tampa, Fl ● 4 month Contract to hire, Moved to permanent role Jan 2016 ● Lead Developer on Insurance Lead Retrieval Web Application ● Migrated .Net3.5 Web Form Insurance application to ReactJS/MVC5/Entity Framework 6 web Application. ● Started automated unit testing environment used during post deployment