Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System

In an intelligent tutoring system (ITS), the domain expert should provide\ud relevant domain knowledge to the tutor so that it will be able to guide the\ud learner during problem solving. However, in several domains, this knowledge is\ud not predetermined and should be captured or learned from expert users as well as\ud intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud techniques can help to build this domain intelligence in ITS. This paper proposes\ud a framework to capture problem-solving knowledge using a promising approach\ud of data and knowledge discovery based on a combination of sequential pattern\ud mining and association rules discovery techniques. The framework has been implemented\ud and is used to discover new meta knowledge and rules in a given domain\ud which then extend domain knowledge and serve as problem space allowing\ud the intelligent tutoring system to guide learners in problem-solving situations.\ud Preliminary experiments have been conducted using the framework as an alternative\ud to a path-planning problem solver in CanadarmTutor

"How May I Help You?": Modeling Twitter Customer Service Conversations Using Fine-Grained Dialogue Acts

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand trends in customer and agent behavior for the purpose of automating customer service interactions. In this work, we develop a novel taxonomy of fine-grained "dialogue acts" frequently observed in customer service, showcasing acts that are more suited to the domain than the more generic existing taxonomies. Using a sequential SVM-HMM model, we model conversation flow, predicting the dialogue act of a given turn in real-time. We characterize differences between customer and agent behavior in Twitter customer service conversations, and investigate the effect of testing our system on different customer service industries. Finally, we use a data-driven approach to predict important conversation outcomes: customer satisfaction, customer frustration, and overall problem resolution. We show that the type and location of certain dialogue acts in a conversation have a significant effect on the probability of desirable and undesirable outcomes, and present actionable rules based on our findings. The patterns and rules we derive can be used as guidelines for outcome-driven automated customer service platforms.Comment: 13 pages, 6 figures, IUI 201

AGROINDUSTRIALIZATION IN EMERGING MARKETS: OVERVIEW AND STRATEGIC CONTEXT

This article offers an overview for a special issue on agroindustrialization. It reviews eleven articles analyzing the agroindustrialization process in Latin America and Asia. It sets out a conceptual framework from the organizational economics and strategic management literature to enhance the understanding of the process of agroindustrialization from a competitive strategy point of view.Agribusiness, Industrial Organization,

The Ideal Candidate. Analysis of Professional Competences through Text Mining of Job Offers

The aim of this paper is to propose analytical tools for identifying peculiar aspects of job market for graduates. We propose a strategy for dealing with daa tat have different source and nature

Generating indicative-informative summaries with SumUM

We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies

Machine Learning of Generic and User-Focused Summarization

A key problem in text summarization is finding a salience function which determines what information in the source should be included in the summary. This paper describes the use of machine learning on a training corpus of documents and their abstracts to discover salience functions which describe what combination of features is optimal for a given summarization task. The method addresses both "generic" and user-focused summaries.Comment: In Proceedings of the Fifteenth National Conference on AI (AAAI-98), p. 821-82