Abstract
In Natural Language Processing, large annotated data sets are needed to train language models using supervised machine learning methods. To obtain such labeled data sets, time consuming manual annotation is required. To facilitate this process, we propose a SOM-based approach: The SOM sorts the data through unsupervised training, mapping the space of linguistic features to a 2D-grid. The grid visualization is used for efficient interactive labeling of the data clusters. In addition, the interactive SOM visualization allows computational linguists to explore the topology of the feature space and design new features.
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Burkovski, A., Kessler, W., Heidemann, G., Kobdani, H., Schütze, H. (2011). Self Organizing Maps in NLP: Exploration of Coreference Feature Space. In: Laaksonen, J., Honkela, T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21566-7_23
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DOI: https://doi.org/10.1007/978-3-642-21566-7_23
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