Cec2010 araujo santamaria
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The document discusses an evolutionary algorithm approach for evolving natural language grammars without supervision. It aims to extract the set of grammar rules underlying a language's constructions from unlabeled text. The algorithm works by incrementally constructing a grammar by considering sentences one by one and evolving parse trees for each through genetic operations. Experimental results on the Penn Treebank show the approach performs comparably to other unsupervised grammar induction systems, though there is room for improvement in the fitness function. Future work could refine the model and test it on other languages and corpora.
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- 6. GI Sintactically annotated texts S NP VP NP NN NNS VP VBD PP PP IN NNP S NP VP NP NP NP NN NNS VBD PP Factory payrolls fell IN NNP in september
- 9. The inferred grammar depends on the type of the texts : newspapers, tales, poetry,...
- 16. Individuals : possible parse trees of the sentence (the leaves the whole sequence of POS tags of the sentence).
- 17. Initial population : randomly created.
- 22. Grouping : Convert in constituent a randomly sequence of POS tags
- 23. Group-ungroup : group and ungroup at the same time
- 25. Measures : recall, precision and their armonic mean F1.
- 26. Bracked representation of the sentences:
- 29. Recall : Number of brackets in the correct tree which are in the evaluated one
- 30. F1:
- 34. The results are stable for different constituent sizes
- 35. It performs similarly than more refined systems.
- 36. There are a lot of room for improvement in the fitness function.