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Unsupervised Topic and Aspect 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. Besides previously known k-mean algorithms on word embeddings, a new form of attention clustering emerged recently (https://www.aclweb.org/anthology/P17-1036) showed good qualitative results and should be further evaluated on linguistic, spoken narrative data

Task In this work, the student(s) will implement unsupervised topic and aspect detection utilising Attention-Clustering (https://github.com/ruidan/Unsupervised-Aspect-Extraction) and compares the result to previously common k-means on word embeddings on two different sentiment databases (SEWA, EmCaR).
Utilises Keras, Tensorflow/Theano, Attention Neural Networks
Requirements Advanced knowledge in machine learning and natural language processing, good programming skills (e.g. Python, C++)
Languages German or English
Supervisor Lukas Stappen, M. Sc. (lukas.stappen@informatik.uni-augsburg.de)