| Challenge: | Existing graph-based constituent parsing methods generate hidden nodes with the dummy label inside the n-ary nodes to transform the tree into a binary tree for prediction. |
| Approach: | They propose a graph-based constituent parsing framework that uses a 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate. |
| Outcome: | The proposed framework obtains the F1 of 95.92% and 92.50% on the datasets of PTB and CTB 5.1 respectively. |
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| Challenge: | Existing dependency parsing models for Arabic use complementary annotations, CATiB and UD treebanks, and partially created trees for one annotation are also available to the other as features for the score function. |
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Please Mind the Root: Decoding Arborescences for Dependency Parsing (2020.emnlp-main)
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| Challenge: | Constituent parsing is a core problem in NLP where the goal is to obtain the syntactic structure of sentences expressed as a phrase structure tree. |
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Graph-based Dependency Parsing with Graph Neural Networks (P19-1)
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On Finding the K-best Non-projective Dependency Trees (2021.acl-long)
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| Challenge: | Existing work on finding the one-best dependency tree has not extended this to finding the K-best tree. |
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