Showing papers on "Chatbot published in 2013"

Toward the implementation of a topic specific dialogue based natural language chatbot as an undergraduate advisor

Abstract: In this work, we explain the design of a chat robot that is specifically tailored for providing FAQBot system for university students and with the objective of an undergraduate advisor in student information desk. The chat robot accepts natural language input from users, navigates through the Information Repository and responds with student information in natural language. In this paper, we model the Information Repository by a connected graph where the nodes contain information and links interrelates the information nodes. The design semantics includes AIML (Artificial Intelligence Mark up Language) specification language for authoring the information repository such that chat robot design separates the information repository from the natural language interface component. Correspondingly, in the experiment, we constructed three experimental systems (a pure dialog systems associated with natural language knowledge based entries, a domain knowledge systems engineered with information content and a hybrid system, combining dialog and domain knowledge). Consequently, the information repository can easily be modified and focussed on particular topic without recreating the code design. Experimental parameters and outcome suggests that topic specific dialogue coupled with conversational knowledge yield the maximum dialogue session than the general conversational dialogue.

Using Learning Analytics to Understand the Design of an Intelligent Language Tutor – Chatbot Lucy

Abstract: the goal of this article is to explore how learning analytics can be used to predict and advise the design of an intelligent language tutor, chatbot Lucy. With its focus on using student-produced data to understand the design of Lucy to assist English language learning, this research can be a valuable component for language-learning designers to improve second language acquisition. In this article, we present students’ learning journey and data trails, the chatting log architecture and resultant applications to the design of language learning systems.

Chatbot technology: A possible means of unlocking student potential to learn how to learn

Abstract: This paper explores the possibility of implementing a constructivist learning environment using chatbot technology as a basis of enabling students acquire global economy and technological information age skills and competencies (21 st century skills) within the context of a developing country. The suggested approach is to integrate chatbot technology into the prevailing teaching-learning environment taking into consideration enabling and constraining factors. Social constructivism provides the basis for concretization of this approach, where social interaction plays a fundamental role in the development of cognition, with mediation using cultural tools and scaffolding contributing to the process of learning.

Detecting Deceptive Chat-Based Communication Using Typing Behavior and Message Cues

Abstract: Computer-mediated deception is prevalent and may have serious consequences for individuals, organizations, and society. This article investigates several metrics as predictors of deception in synchronous chat-based environments, where participants must often spontaneously formulate deceptive responses. Based on cognitive load theory, we hypothesize that deception influences response time, word count, lexical diversity, and the number of times a chat message is edited. Using a custom chatbot to conduct interviews in an experiment, we collected 1,572 deceitful and 1,590 truthful chat-based responses. The results of the experiment confirm that deception is positively correlated with response time and the number of edits and negatively correlated to word count. Contrary to our prediction, we found that deception is not significantly correlated with lexical diversity. Furthermore, the age of the participant moderates the influence of deception on response time. Our results have implications for understanding deceit in chat-based communication and building deception-detection decision aids in chat-based systems.

An Architecture to Develop Multimodal Educative Applications with Chatbots

Abstract: Animated characters are beginning to be used as pedagogical tools, as they have the power to capture students' attention and foster their motivation for discovery and learning. However, in order for them to be widely employed and accepted as a learning resource, they must be easy to use and friendly. In this paper we present an architecture that facilitates building interactive pedagogical chatbots that can interact with students in natural language. Our proposal provides a modular and scalable framework to develop such systems efficiently. Additionally, we present Geranium, a system that helps children to appreciate and protect their environment with an interactive chatbot developed following our scheme.

Introducing Chatbots in Libraries

Abstract: Chapter 1 of Library Technology Reports (vol. 49, no. 8), "Streamlining Information Services Using Chatbots, " presents a brief history of chatbots, computer programs that use natural language to interact with users. They have existed for nearly fifty years and have been used in libraries since the mid-2000s; chatbots from ELIZA (1966) to Pixel (2010) are introduced. ********** As many libraries continue to see reductions in funding, we are increasingly seeing technology as a way to make up for budget shortfalls. In the circulation context, online patron account management, self-registration, and self-serve checkout stations are examples of this trend. Since requests for basic library information (including locations, hours, and policies) and for specific materials or resources predominate among chat and IM inquiries of libraries, (1) "chatbots" or "virtual agents" offer a self-service option for our online customers in the context of information services. Such virtual agents are becoming a familiar feature on many websites. These user-friendly implementations of artificial intelligence have enjoyed remarkable success in corporate and government sectors and are projected to continue to grow in popularity. Indeed, a VirtuOz/CCM Benchmark Group study projects a "400% increase in virtual agents between 2011 and 2014." (2) Chatbots are able to respond to a remarkable variety of customer inquiries with correct information specifically tailored to the customer's needs through the use of natural language processing (NLP). Warschauer and Healy define NLP as "the process of a computer extracting meaningful information from natural language input and/or producing natural language output." (3) The process of searching databases or catalogs usually requires the user to compose a search for the information needed, conforming to the structures and language defined by the target data source. A chatbot using NLP, on the other hand, allows users to pose a question as they would to another human being. The responsibility of locating the needed information shifts from the user to the programmer of the chatbot. The chatbot designer creates a structure that leads the user through a question-and-answer dialogue to discover the information needed and to provide it. This process can also address the problems created by library terminology or jargon with which the user may not be familiar. In addition, regular review of the chatbot's conversation logs allows the designer to monitor the types of questions and the terminology used to pose them and to update the responses provided by the chatbot and the language it recognizes. This is why the chatbot can be particularly convenient and helpful to those patrons who are least familiar with the library and its services. * Chatbots are also consistently patient and polite and remain unruffled by rude customers, high traffic, or repeated requests for the same information. In a discussion of chatbots, Christansen suggests that chatbots: ** were selected more frequently than other forms of digital reference ** made asking questions easier (by providing a natural language interface) ** provided instant responses ** were anonymous (which encouraged shy users or those who thought their questions might be "stupid") ** provided a marketing tool for reference services (4) Christiansen's final observation is particularly noteworthy. If we enhance our hot with links to our various resources (including our staff directory, our catalog, and our online databases and reference tools), we can introduce our customers to our whole gamut of useful resources. In addition, chatbots offer the ability to personalize the user experience, welcoming patrons and putting them at ease. This is highlighted in a SitePal case study, which documented a 40 percent increase in sales at a company that incorporated a chatbot to welcome users to its website and guide them through its broad inventory of products. …

Auto-reply email system and method with personalized content

Abstract: An auto-reply electronic mail message with personalized content. Text is extracted from an inbound email message. The text is used for chatbot input messages. Chatbot output messages are generated. The chatbot output messages are composed. The composed messages are formed into a responsive outbound email communication.

Design and Implementation of Conversational Agents for Harvesting Feedback in eLearning Systems

Abstract: Traditionally conversational interfaces, such as chatbots, have been created in two distinct ways. Either by using natural language parsing methods or by creating conversational trees that utilise the natural Zipf curve distribution of conversations using a tool like AIML. This work describes a hybrid method where conversational trees are developed for specific types of conversations, and then through the use of a bespoke scripting language, called OwlLang, domain knowledge is extracted from semantic web ontologies. New knowledge obtained through the conversations can also be stored in the ontologies allowing an evolving knowledge base. The paper describes two case studies where this method has been used to evaluate TEL by surveying users, firstly about the experience of using a learning management system and secondly about students' experiences of an intelligent tutor system within the I-TUTOR project.

Haciéndolo realidad: ganador del Premio Loebner del diseño chatbot

Abstract: For the last three years, our chatbots have come in 1st twice and 2nd once in the Loebner Prize Contest, with a different persona each year (Suzette, Rosette, Angela). Suzette even fooled a human judge. A world-class chatbot should tell the story of its life, have a consistent personality, and respond emotionally. It takes a lot of script. And it takes a powerful engine designed to support natural language processing in a variety of ways and make it relatively easy to author all that script. This paper briefly discusses ChatScript, the open-source Natural Language scripting language and engine running our bots. Then it looks at how we construct chatbots and what we have learned.

Virtual personal assistant

Abstract: 2 Abstract This report discusses ways in which new technology could be harnessed to create an intelligent Virtual Personal Assistant (VPA) with a focus on user- based data. It will look at examples of intelligent programs with natural language processing that are currently available, with different categories of support, and examine the potential usefulness of one specific piece of software as a VPA. This engages the ability to communicate socially through natural language processing, holding and analysing data within the context of the user. It is suggested that new technologies may soon make the idea of virtual personal assistants a reality. Experiments conducted on this system, combined with user testing, have provided evidence that a basic program with natural language processing algorithms in the form of a VPA, with basic natural language processing and the ability to function without the need for other type of human input (or programming) may already be viable. Background Recent events have focused much attention on personal data and the parlous nature of boundaries previously drawn between private and public systems. Many people now wonder who has access to data resulting from unwise posts to social media sites, or monitoring of supposedly private emails and telephone conversations. As the focus of supporting systems is shifting towards holding more data within 'the Cloud', the privacy and security of this data has become a cause for concern. With this area of social concern in mind, the idea of a VPA becomes attractive as it changes the focus of the supporting system to the contextual sphere under private control of the user. We have devised a hypothesis that a chatbot with natural language algorithms and localised data could be trained to function as a VPA with user based data control. This will require the program to be able to create metadata by forming links between data it is given and provide contextual outputs during the interaction that the user finds useful. When looking at a number of currently available intelligent programs with natural language processing capabilities, many examples can be found in everyday life filling a variety of roles. The intelligent bot Siri can be found as standard on Apple mobile devices now and is considered a core component on these devices. Siri is a

AIML Knowledge Base Construction from Text Corpora

Abstract: Text mining (TM) and computational linguistics (CL) are computationally intensive fields where many tools are becoming available to study large text corpora and exploit the use of corpora for various purposes. In this chapter we will address the problem of building conversational agents or chatbots from corpora for domain-specific educational purposes. After addressing some linguistic issues relevant to the development of chatbot tools from corpora, a methodology to systematically analyze large text corpora about a limited knowledge domain will be presented. Given the Artificial Intelligence Markup Language as the “assembly language” for the artificial intelligence conversational agents we present a way of using text corpora as seed from which a set of “source files” can be derived. More specifically we will illustrate how to use corpus data to extract relevant keywords, multiword expressions, glossary building and text patterns in order to build an AIML knowledge base that could be later used to build interactive conversational systems. The approach we propose does not require deep understanding techniques for the analysis of text.

Impact Study of Nonverbal Facial Cues on Spontaneous Chatting with Virtual Humans

Abstract: JVRB, 10(2013), no. 6. - Non-verbal communication (NVC) is considered to represent more than 90 percent of everyday communication. In virtual world, this important aspect of interaction between virtual humans (VH) is strongly neglected. This paper presents a user-test study to demonstrate the impact of automatically generated graphics-based NVC expression on the dialog quality: first, we wanted to compare impassive and emotion facial expression simulation for impact on the chatting. Second, we wanted to see whether people like chatting within a 3D graphical environment. Our model only proposes facial expressions and head movements induced from spontaneous chatting between VHs. Only subtle facial expressions are being used as nonverbal cues - i.e. related to the emotional model. Motion capture animations related to hand gestures, such as cleaning glasses, were randomly used to make the virtual human lively. After briefly introducing the technical architecture of the 3D-chatting system, we focus on two aspects of chatting through VHs. First, what is the influence of facial expressions that are induced from text dialog? For this purpose, we exploited an emotion engine extracting an emotional content from a text and depicting it into a virtual character developed previously [GAS11]. Second, as our goal was not addressing automatic generation of text, we compared the impact of nonverbal cues in conversation with a chatbot or with a human operator with a wizard of oz approach. Among main results, the within group study -involving 40 subjects- suggests that subtle facial expressions impact significantly not only on the quality of experience but also on dialog understanding.

An investigation of student's attitude towards use of chatbot technology in Instruction: the case of Knowie in a selected high school

Abstract: A chatbot named Knowie was implemented on a platform based on the open source software Ubuntu, Python, JDK, and PyAIML. A class of students was then given an opportunity to ask questions of and chat with the bot on a topic they were being taught in class over a period of four weeks. They then completed a questionnaire after the intervention with items designed to determine their attitude towards their experience and to elicit their suggestions on how the chatbot could better be improved and used to suit their requirements.

Chatbot Creation Options

Abstract: Chapter 2 of Library Technology Reports (vol. 49, no. 8), "Streamlining Information Services Using Chatbots," introduces AIML and ChatScript, the two most viable languages for creating a chatbot While their basic structure and syntax are markedly different, either may be used effectively, and both offer their own advantages. ********** There are a number of coding options available to use in creating your own bot. The markup or scripting language you choose will depend on your skill and experience, the amount of time you have available, and the functionality you're trying to create. At present, the best choices are AIML (Program Z or Program O) and ChatScript. We'll examine each in turn. AIML (Artificial Intelligence Markup Language) AIML is the starting place for many who are interested in chatbots or natural language processing. AIML was created in 1995 by Dr. Richard Wallace and is the basis for numerous chatbots, including the original Emma the Catbot, the University of Nebraska's Pixel, Adeena Mignona's Zoe, and Steve Worswick's Mitsuku. AIML's great virtue is its simplicity; it's easy to learn and to implement. AIML is an XML dialect, so if you're familiar with XML or HTML, you'll be able to learn AIML quickly. You can write AIML using Notepad, WordPad, or a spreadsheet-style AIML editor like Simple AIML Editor from RIOT Software. AIML is based on pattern matching. Essentially, the data making up an AIML bot's "brain" take the form of a very large decision tree. User input is first preprocessed and then matched in order against the nodes of the tree. When input finds a match, the bot will execute an action, such as responding or opening a web page. Simple AIML Editor http://riotsw.com/sae.html AIML does have some drawbacks, however. AIML's pattern matching is relatively weak, which means the content you create has the potential to match a range of input and return incorrect or meaningless responses. While authoring content is easy, a large amount of content is needed to create a convincing bot, somewhere in the range of 60,000 + categories. Each question or concept in the bot's knowledge base requires multiple categories to match permutations of the question and to ensure a correct response. For instance, there are many ways to ask, "What time does the library open?": "When do you open?" "When are you opening today?" "What time do you open?" "Will you be open today?" You can easily add to this list. A category is required to match each variation: WHAT TIME DOES THE LIBRARY OPEN HOURS WHEN DO YOU OPEN HOURS WHEN ARE YOU OPENING TODAY HOURS WHAT TIME DO YOU OPEN HOURS WILL YOU BE OPEN TODAY HOURS In order to understand how an input will match or fail, you need to be familiar with all of the categories dealing with each question or concept in the bot's knowledge base. One cannot look at an individual AIML category and know what input it will match. Writing code to distinguish between fine shades of meaning can be tricky, and the time required to maintain and debug an AIML knowledge base can be considerable. Before you become too discouraged, keep in mind that AIML has been used to create successful library bots. …

ATTORNEY 209: A Virtual Assistant Adviser for Family-Based Cases

Abstract: This paper introduces ATTORNEY 209: A Virtual Assistant Adviser for Family-Based Cases. It is a conversational chatbot which is designed to handle initial assessment for families who need legal guidance regarding family cases such as child custody and legal separation. Chart Parsing Algorithm, known in Natural Language Processing, is the approach implemented in this study. It is suitable for ambiguous grammar and uses the dynamic programming approach—partial hypothesized results are stored in a structure called a chart and can be re-used. This eliminates backtracking and prevents a combinatorial

C4 (C quad): Development of the Application for Language Learning Based on Social and Cognitive Presences

Abstract: This research aims to develop collaborative language learning systems based on social and cognitive presence for learning settings out of class, and evaluate their effects on learning attitude and performance. The main purpose of this system is focusing on the building of a learning community, therefore the Community of Inquiry (CoI) framework suggested by Garrison, Anderson, and Archer (2000) was considered to design this system. This system “C4” (spelled out as C quad) consists of three functions: chatbot, constitutive chat, and contribution visualization for the enhancement of social and cognitive presence. In this paper, we explain system design and architecture, and discuss future work.

Artificial conversational companions

Abstract: This document describes the problem statement, the methodological framework, the current state of the work and the expected contribution of my doctoral dissertation. The main focus of my dissertation is long-term interaction with an Artificial Conversational Companion in the context of conversation training for second language acquisition. I use a data-driven approach and conversation analysis methods to build computational models for long-term interaction as a meaningful activity. I work on the concept of interaction profiles for human-agent interaction. The resulting models will be integrated in an AIML-based chatbot that helps to practice conversation in a foreign language.

Chapter 3: Basic Components of AIML

Abstract: Chapter 3 of Library Technology Reports (vol. 49, no. 8), “Streamlining Information Services Using Chatbots,” describes elements in AIML. The basic structure of AIML is simple; one can create a working chatbot using a small number of AIML tags .

Clifford Rotors for Conceptual Representation in Chatbots

Abstract: In this abstract we introduce an unsupervised sub-symbolic natural language sentences encoding procedure aimed at catching and representing into a Chatbot Knowledge Base (KB) the concepts expressed by an user interacting with a robot.