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What and Who
Title:Personal Knowledge Extraction: What Can Be Inferred From What You Say and Do
Speaker:Paramita Mirza
coming from:Max-Planck-Institut für Informatik - D5
Speakers Bio:
Event Type:Joint Lecture Series
Visibility:D1, D2, D3, INET, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Language:English
Date, Time and Location
Date:Wednesday, 6 November 2019
Time:12:15
Duration:60 Minutes
Location:Saarbrücken
Building:E1 5
Room:002
Abstract
Despite recent advances in natural language processing and generation, communication between humans and machines is in still its infancy. Existing intelligent home and mobile assistant technologies excel at scripted tasks such as weather or news reports and music control, yet typically fail at more advanced personalization. This calls for a centralized repository for personal knowledge about each user, which will then be a distant source of background knowledge for personalization in downstream applications. Such personal knowledge repository will be beneficial as a reusable asset; it should be both explainable and scrutable, giving full control to the owning user on editing and sharing stored information with selected service providers.

In this talk, I will discuss our efforts on automated personal knowledge extraction. We can easily obtain personal knowledge of famous people from biographies or news articles, however, such resources are not available for ordinary users. Hence, we turn to the task of inferring personal attributes from users' utterances in conversations, e.g., guessing a person's occupation from "I was sitting the whole day in front of my computer today, trying to finish a grant proposal for my research." I will highlight our Hidden Attribute Models (HAM) to solve the problem, a neural architecture leveraging attention mechanisms and embeddings, as well as an ongoing work on its extension to address challenging attributes such as hobbies and travel preferences with wide sets of multi-faceted attribute values. Finally, I will present an outlook on what we can further infer from users' activities, particularly in relation with their mood and emotion.
Contact
Name(s):Jennifer Müller
Phone:2900
EMail:--email address not disclosed on the web
Video Broadcast
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  • Jennifer Müller, 10/28/2019 10:39 AM
  • Jennifer Müller, 08/26/2019 11:50 AM -- Created document.