Extracting references from political speech auto-transcripts
published: Dec. 1, 2017, recorded: August 2017, views: 705
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This paper presents an unsupervised method for counting references in noisy auto-transcribed political speeches. Transcriptions are vectorized using learned embeddings which are then clustered using k-means resulting in groups of words which represent highly granular, specific topics within the text. Words from each cluster are then extracted from each transcript, counted, and arranged for time-series analysis. The approach finds semantically coherent topics representing specific references despite transcription inaccuracies. We use this framework to extract references from over 400 political speech transcriptions from a 2016 U.S. presidential campaign.
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