Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a search procedure to discover optimal feature sets for dependency parsers. The search applies to the shift-reduce algorithm and the feature sets are extracted from the parser configuration. The initial feature is limited to the first word in the input queue. Then, the procedure uses a set of rules founded on the assumption that topological neighbors of significant features in the dependency graph may also have a significant contribution. The search can be fully automated and the level of greediness adjusted with the number of features examined at each iteration of the discovery procedure.#R##N##R##N#Using our automated feature discovery on two corpora, the Sdish corpus in CoNLL-X and the English corpus in CoNLL 2008, and a single parser system,  could reach results comparable or better than the best scores reported in these evaluations. The CoNLL 2008 test set contains, in addition to a Wall Street Journal (WSJ) section, an out-of-domain sample from the Brown corpus. With sets of 15 features,  obtained a labeled attachment score of 84.21 for Sdish, 88.11 on the WSJ test set, and 81.33 on the Brown test set.
['Peter Nilsson', 'Pierre Nugues']
I am writing a research paper on a idea Automatic Discovery of Feature Sets for Dependency Parsing
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Discovery of Feature Sets for Dependency Parsing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  describes a search procedure to discover optimal feature sets for dependency parsers. The search applies to the shift-reduce algorithm and the feature sets are extracted from the parser configuration. The initial feature is limited to the first word in the input queue. Then, the procedure uses a set of rules founded on the assumption that topological neighbors of significant features in the dependency graph may also have a significant contribution. The search can be fully automated and the level of greediness adjusted with the number of features examined at each iteration of the discovery procedure.#R##N##R##N#Using our automated feature discovery on two corpora, the Sdish corpus in CoNLL-X and the English corpus in CoNLL 2008, and a single parser system,  could reach results comparable or better than the best scores reported in these evaluations. The CoNLL 2008 test set contains, in addition to a Wall Street Journal (WSJ) section, an out-of-domain sample from the Brown corpus. With sets of 15 features,  obtained a labeled attachment score of 84.21 for Sdish, 88.11 on the WSJ test set, and 81.33 on the Brown test set."}]