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144
Bursty and Hierarchical Structure in Streams
, 2002
"... A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade aw ..."
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Cited by 394 (2 self)
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A fundamental problem in text data mining is to extract meaningful structure from document streams that arrive continuously over time. E-mail and news articles are two natural examples of such streams, each characterized by topics that appear, grow in intensity for a period of time, and then fade away. The published literature in a particular research field can be seen to exhibit similar phenomena over a much longer time scale. Underlying much of the text mining work in this area is the following intuitive premise --- that the appearance of a topic in a document stream is signaled by a "burst of activity," with certain features rising sharply in frequency as the topic emerges.
Taking Email to Task: The Design and Evaluation of a Task Management Centered Email Tool
, 2003
"... Email has come to play a central role in task management, yet email tool features have remained relatively static in recent years, lagging behind users ’ evolving practices. The Taskmaster system narrows this gap by recasting email as task management and embedding task-centric resources directly in ..."
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Cited by 198 (5 self)
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Email has come to play a central role in task management, yet email tool features have remained relatively static in recent years, lagging behind users ’ evolving practices. The Taskmaster system narrows this gap by recasting email as task management and embedding task-centric resources directly in the client. In this paper, we describe the field research that inspired Taskmaster and the principles behind its design. We then describe how user studies conducted with “live ” email data over a two-week period revealed the value of a task-centric approach to email system design and its potential benefits for overloaded users.
Adaptive interfaces and agents
, 2003
"... As its title suggests, this chapter covers a broad range of in-teractive systems. But they all have one idea in common: that it can be worthwhile for a system to learn something about each individual user and adapt its behavior to them in ..."
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Cited by 101 (10 self)
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As its title suggests, this chapter covers a broad range of in-teractive systems. But they all have one idea in common: that it can be worthwhile for a system to learn something about each individual user and adapt its behavior to them in
A personalized system for conversational recommendations,
- Journal of Artificial Intelligence Research,
, 2004
"... Abstract Increased computing power and the Web have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each ..."
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Cited by 75 (1 self)
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Abstract Increased computing power and the Web have made information widely accessible. In turn, this has encouraged the development of recommendation systems that help users find items of interest, such as books or restaurants. Such systems are more useful when they personalize themselves to each user's preferences, thus making the recommendation process more efficient and effective. In this paper, we present a new type of recommendation system that carries out a personalized dialogue with the user. This system -the Adaptive Place Advisor -treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. The system incorporates a user model that contains item, attribute, and value preferences, which it updates during each conversation and maintains across sessions. The Place Advisor uses both the conversational context and the user model to retrieve candidate items from a case base. The system then continues to ask questions, using personalized heuristics to select which attribute to ask about next. Then, when only a few items remain, it presents them to the user in a personalized order. We report experimental results demonstrating the effectiveness of user modeling in reducing the time and number of interactions required to find a satisfactory item.
Quality versus quantity: E-mail-centric task management and its relation with overload
, 2005
"... It is widely acknowledged that many professionals suffer from “e-mail overload.” This articlepresents findings fromin-depth fieldwork that examined thisphenome-non, uncovering six key challenges of task management in e-mail. Analysis of quali-tativeandquantitativedata suggests that it is not simply ..."
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Cited by 69 (5 self)
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It is widely acknowledged that many professionals suffer from “e-mail overload.” This articlepresents findings fromin-depth fieldwork that examined thisphenome-non, uncovering six key challenges of task management in e-mail. Analysis of quali-tativeandquantitativedata suggests that it is not simply thequantitybut also thecol-
A hybrid learning system for recognizing user tasks from desktop activities and email messages
- In Proc. of IUI-06
, 2006
"... The TaskTracer system seeks to help multi-tasking users manage the resources that they create and access while carrying out their work activities. It does this by associating with each user-defined activity the set of files, folders, email messages, contacts, and web pages that the user accesses whe ..."
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Cited by 69 (13 self)
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The TaskTracer system seeks to help multi-tasking users manage the resources that they create and access while carrying out their work activities. It does this by associating with each user-defined activity the set of files, folders, email messages, contacts, and web pages that the user accesses when performing that activity. The initial TaskTracer system relies on the user to notify the system each time the user changes activities. However, this is burdensome, and users often forget to tell TaskTracer what activity they are working on. This paper introduces TaskPredictor, a machine learning system that attempts to predict the user’s current activity. TaskPredictor has two components: one for general desktop activity and another specifically for email. TaskPredictor achieves high prediction precision by combining three techniques: (a) feature selection via mutual information, (b) classification based on a confidence threshold, and (c) a hybrid design in which a Naive Bayes classifier estimates the classification confidence but where the actual classification decision is made by a support vector machine. This paper provides experimental results on data collected from Task-Tracer users.
Learning to classify email into speech acts
- In Proceedings of Empirical Methods in Natural Language Processing
, 2004
"... It is often useful to classify email according to the intent of the sender (e.g., "propose a meeting", "deliver information"). We present experimental results in learning to classify email in this fashion, where each class corresponds to a verb-noun pair taken from a predefined o ..."
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Cited by 62 (8 self)
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It is often useful to classify email according to the intent of the sender (e.g., "propose a meeting", "deliver information"). We present experimental results in learning to classify email in this fashion, where each class corresponds to a verb-noun pair taken from a predefined ontology describing typical “email speech acts”. We demonstrate that, although this categorization problem is quite different from “topical ” text classification, certain categories of messages can nonetheless be detected with high precision (above 80%) and reasonable recall (above 50%) using existing text-classification learning methods. This result suggests that useful tasktracking tools could be constructed based on automatic classification into this taxonomy. 1
Email Classification with Co-Training
, 2002
"... The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algorithm that uses unlabeled data along with a few labeled examples to boost the performance of a classifier. We experiment ..."
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Cited by 58 (0 self)
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The main problems in text classification are lack of labeled data, as well as the cost of labeling the unlabeled data. We address these problems by exploring co-training - an algorithm that uses unlabeled data along with a few labeled examples to boost the performance of a classifier. We experiment with co-training on the email domain. Our results show that the performance of co-training depends on the learning algorithm it uses. In particular, Support Vector Machines significantly outperforms Naive Bayes on email classification.
ifile: An Application of Machine Learning to E-Mail Filtering
- Proc. KDD Workshop on Text Mining
, 2000
"... The rise of the World Wide Web and the ever-increasing amounts of machine-readable text has caused text classification to become a important aspect of machine learning. One specific application that has the potential to affect almost every user of the Internet is e-mail filtering. The WorldTalk Corp ..."
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Cited by 52 (0 self)
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The rise of the World Wide Web and the ever-increasing amounts of machine-readable text has caused text classification to become a important aspect of machine learning. One specific application that has the potential to affect almost every user of the Internet is e-mail filtering. The WorldTalk Corporation estimates that over 60 million business people use e-mail [6]. Many more use e-mail purely on a personal basis and the pool of e-mail users is growing daily. And yet, automated techniques for learning to filter e-mail have yet to significantly affect the e-mail market. Here, I attack problems that plague practical e-mail ltering and suggest solutions that will bring us closer to the acceptance of using automated classification techniques to filter personal e-mail. I also present a filtering system, ifile, that is both effective and efficient, and which has been adapted to a popular e-mail client. Results are presented from a number of experiments and show that a system such as ifile could become a...
Athena: Mining-based interactive management of text databases
- International Conference on Extending Database Technology
, 2000
"... Abstract. We describe Athena: a system for creating, exploiting, and maintaining a hierarchy of textual documents through interactive miningbased operations. Requirements of any such system include speed and minimal end-user e ort. Athena satis es these requirements through linear-time classi cation ..."
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Cited by 43 (3 self)
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Abstract. We describe Athena: a system for creating, exploiting, and maintaining a hierarchy of textual documents through interactive miningbased operations. Requirements of any such system include speed and minimal end-user e ort. Athena satis es these requirements through linear-time classi cation and clustering engines which are applied interactively to speed the development of accurate models. Naive Bayes classi ers are recognized to be among the best for classifying text. We show that our specialization of the Naive Bayes classi er is considerably more accurate (7 to 29 % absolute increase in accuracy) than a standard implementation. Our enhancements include using Lidstone's law of succession instead of Laplace's law, under-weighting long documents, and over-weighting author and subject. We also present a new interactive clustering algorithm, C-Evolve, for topic discovery. C-Evolve rst nds highly accurate cluster digests (partial clusters), gets user feedback to merge and correct these digests, and then uses the classi cation algorithm to complete the partitioning of the data. By allowing this interactivity in the clustering process, C-Evolve achieves considerably higher clustering accuracy (10 to 20 % absolute increase in our experiments) than the popular K-Means and agglomerative clustering methods. 1
