Abstract
Fine tuning features for NP chunking is a difficult task. The effects of a modification are sometimes unpredictable. The tuning process with a (un)supervised learning algorithm does not produce necessarily better results. An online toolkit was developed for this scenario highlighting critical areas in training data, which may pose a challenge for the learning algorithm: irregular data, exceptions in trends, unusual property values. This overview of problematic data might inspire the linguist to enhance the data (for example by dividing a class into more detailed classes). The kit was tested on English and Hungarian corpora. Results show that the preparation of datasets for NP chunking is accelerated effectively, which result in better F-scores. The toolkit runs on a simple browser and its usage poses no difficulties for non-technical users. The tool combines the abstraction ability of a linguist and the power of a statistical engine.
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Acknowledgments
I would like to show my gratitude to Dr Gábor Prószéky for motivation, and I thank Dr Nóra Wenszky, Zsuzsanna Balogh, Borbála Siklósi and the anonym reviewers for their comments.
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Endrédy, I. (2018). Improving Chunker Performance Using a Web-Based Semi-automatic Training Data Analysis Tool. In: Vetulani, Z., Mariani, J., Kubis, M. (eds) Human Language Technology. Challenges for Computer Science and Linguistics. LTC 2015. Lecture Notes in Computer Science(), vol 10930. Springer, Cham. https://doi.org/10.1007/978-3-319-93782-3_21
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