AG 1, AG 2, AG 3, INET, AG 4, AG 5, D6, SWS, RG1, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

Personal knowledge is a versatile resource that is valuable for a wide range of downstream applications, such as chatbot assistants, recommendation models and personalized search. A Personal Knowledge Base, populated with personal facts, such as demographic information, interests and interpersonal relationships, is a unique endpoint for storing and querying personal knowledge, providing users with full control over their personal information. To alleviate users from extensive manual effort to build such personal knowledge base, we can leverage automated extraction methods applied to the textual content of the users, such as dialogue transcripts or social media posts. Such conversational data is abundant but challenging to process and requires specialized methods for extraction of personal facts.
In this dissertation we address the acquisition of personal knowledge from conversational data. We propose several novel deep learning models for inferring speakers' personal attributes, such as demographic facts, hobbies and interpersonal relationships. Experiments with various conversational texts, including Reddit discussions and movie scripts, demonstrate the viability of our methods and their superior performance compared to state-of-the-art baselines.

Petra Schaaf

+49 681 9325 5000

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