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| Author(s): |
Erick Rocha Fonseca
Total Authors: 1
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| Document type: | Master's Dissertation |
| Press: | São Carlos. |
| Institution: | Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) |
| Defense date: | 2013-04-10 |
| Examining board members: |
André Carlos Ponce de Leon Ferreira de Carvalho;
Sandra Maria Aluisio;
Bento Carlos Dias da Silva
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| Advisor: | João Luis Garcia Rosa |
| Abstract | |
Semantic Role Labeling (SRL) is a subfield of Natural Language Processing (NLP) which began to be explored for English in 2002. Its goal is to detect structures of predicate and arguments in written sentences, which correspond to descriptions of events (usually made by verbs); its participants, such as agents and patients; and circumstances, such as time, place, etc. Many NLP applications, as machine translation and information retrieval, have achieved performance gains by applying SRL as a pre-processing step. For Portuguese, advances in SRL research are still in very early stages. Given that the majority of works found in the literature of this area employ supervised machine learning, a limiting factor has been the absence of labeled data in Portuguese, a problem that only recently was partially solved with the creation of PropBank-Br. This resource follows the annotation model used in PropBank, the main labeled data set employed in the SRL task for English. Even then, PropBank-Br contains less than one tenth of the data instances present in the original PropBank. Another point to be observed is that the most common approach to SRL is based on the extraction of a great amount of information from the input sentences to be used by automatic classifiers. Such approach is extremely dependent on other NLP tools, a particularly undesirable feature in the case of Portuguese, which does not have many freely available resources. On the other hand, another succesful approach found in the literature forgoes the use of explicit linguistic knowledge and associates words to numeric sequences, whose values are adjusted during the training of an artificial neural network. These sequences are then employed by the network in order to perform SRL, and can also be useful for other NLP tasks. This work followed the second method described above. Modifications on this method were implemented and allowed for a performance gain in comparison with its original version when tested on PropBank-Br. The final version of the developed system is ready for use and will be able to help NLP research in Portuguese (AU) | |
| FAPESP's process: | 11/15336-9 - A connectionst approach for semantic role labeling |
| Grantee: | Erick Rocha Fonseca |
| Support Opportunities: | Scholarships in Brazil - Master |