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Unsupervised Neural Topic Detection in Spoken Narratives


Description

Extracting the relevant topics and entities of a conversation is an important part of sentiment analysis that wants to categorize the opinions expressed towards a particular topic. Especially when designing data sets with the aim to make sentiment analysis supervised learnable, a prior extraction of the relevant topics is elementary, since only relevant entities and topics that are frequently used are important to annotate. A new type of neural network clustering emerged recently (https://www.aclweb.org/anthology/P17-1036) that showed good qualitative results and arouses interest in an evaluation of similar, more recently published work

Task In this work, the student(s) identifies and structures the latest research in the field of unsupervised neural learning, with a particular focus on topic and aspect detection for sentiment analysis.
Utilises None, literature review only
Requirements Preliminary knowledge of neural networks and representation learning (word embeddings)
Languages German or English
Supervisor Lukas Stappen, M. Sc. (lukas.stappen@informatik.uni-augsburg.de)