 | From the Series از مجموعه : Email Activity Management: A Machine Learning Approach
Produced by تهيه كننده : Microsoft Research
Date تاريخ : 2006-02-27
Email Activity Management: A Machine Learning Approachdownload دانلود ,ويدئو و اسلايد Video & Slide , از گروه Computer Sience & Engineering كامپيوتر و مهندسی كتابخانه اينترنتي دانش گستران جوان You Research Description توضيح : Our use of ordinary desktop applications is often a manifestation of the activities with which we are engaged. Planning a conference trip involves visits to airline and hotel sites, travel expense forms, etc. Renovating a kitchen involves sketches, product specifications, email with the architect, spreadsheets for tracking expenses, etc. Every enterprise has (often implicit) processes for managing customer queries, requesting maintenance, hiring someone, and so forth. Unfortunately, ordinary desktop applications don't know anything about these activities. Within an enterprise, it may be possible to coerce people to use a specialized workflow system to execute some processes; for example, customer relationship management systems ensure that customer queries are handled properly. But most activities exist entirely in our heads, forcing us to rely on crude techniques such as manual search, file directories, and email folders/threads to remember which documents and data are associated with which activities.
In this talk, I describe techniques for automatically discovering and tracking activities in email. A common theme is the use of machine learning to generalize over previous activities. First, I discuss highly structured activities such as e-commerce transactions. Existing email clients do not understand this structure, so users must manage their transactions by sifting through lists of messages. As a step toward providing high-level support for structured activities, I consider the problem of automatically learning an activity's structure. I formalize activities as finite-state automata, and propose several unsupervised machine learning algorithms in this context.
Second, I discuss less structured activities such as organizing meetings or collaboratively editing documents. I describe machine learning approaches to activity discovery and semantic message analysis. Our key innovation compared to related work is that we exploit the relational structure of these two activities.
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Email Activity Management: A Machine Learning Approachdownload دانلود ,ويدئو و اسلايد Video & Slide , از گروه Computer Sience & Engineering كامپيوتر و مهندسی كتابخانه اينترنتي دانش گستران جوان You Research Speaker(s) اجرا : Nicholas Kushmerick, senior lecturer, School of Computer Science & Informatics, University College Dublin, Ireland
Runtime مدت زمان : 01:06:17
Video Size حجم ويدئو : 135 MB
Number of Slides تعداد اسلايد‌ها : 98 (10 MB)
Email Activity Management: A Machine Learning Approachdownload دانلود ,ويدئو و اسلايد Video & Slide , از گروه Computer Sience & Engineering كامپيوتر و مهندسی كتابخانه اينترنتي دانش گستران جوان You Research
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