Recording sensor data is seldom a perfect process.
Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training.
We address the problem of tuning word embeddings for specific use cases and domains.
In this paper, we also compare the efficiency and accuracy of RECOVDB against state-of-the-art recovery systems.
From our extensive evaluation of 20 architectures, we report a highest score of 71. 6% F1 for the segmentation and classification of 30 topics from the English city domain, scored by our SECTOR LSTM model with bloom filter embeddings and bidirectional segmentation.
There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems.