Abstract
In the previous chapters, we have described the approaches of using the information of bilexical dependencies and subtrees. The approaches make use of bi- and tri-gram lexical subtree structures. It can be extended further. The base features defined over surface words, part-of-speech tags represent more complex tree structures than bilexical dependencies and lexical subtrees.
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- 1.
We ensure that the text used for building the meta-features did not include the sentences of the Penn Treebank.
- 2.
We exclude the sentences of the CTB data from the Gigaword data.
- 3.
We also test the settings of dividing WM into two subtypes: W and M. The results show that both subtypes provide positive results. To simplify, we merge W and M into one category, WM.
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Chen, W., Zhang, M. (2015). Training with Meta-features. In: Semi-Supervised Dependency Parsing. Springer, Singapore. https://doi.org/10.1007/978-981-287-552-5_9
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