By João Silva, Ricardo Ribeiro, Paulo Quaresma, André Adami, António Branco
This publication constitutes the refereed court cases of the twelfth foreign convention on Computational Processing of the Portuguese Language, PROPOR 2016, held in Tomar, Portugal, in July 2016.
The 23 complete papers and 14 brief papers provided during this quantity have been rigorously reviewed and chosen from fifty two submissions. The papers are prepared in topical sections named: language functions, language processing, and language resources.
Read or Download Computational Processing of the Portuguese Language: 12th International Conference, PROPOR 2016, Tomar, Portugal, July 13-15, 2016, Proceedings PDF
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Extra info for Computational Processing of the Portuguese Language: 12th International Conference, PROPOR 2016, Tomar, Portugal, July 13-15, 2016, Proceedings
Rodrigues and P. 33 % of right answers. On the answers that have not been found, we have determined that in a few cases the fault is due to questions depending on information contained in other questions or their answers. There are certainly also many shortcomings in the creation of the triples, mainly on the phrase chunks that are close together, as opposed to the dependency chunks, that should and must be addressed, in order to improve and create more triples. Furthermore, there are questions that refer to entities that fail to be identiﬁed as such by our system, an so no triples were created for them when processing the sentences.
Also, we will implement successful features for the English language, cited by , such as average sentence length and features from the language model of our corpus. Moreover, and more importantly, we will implement a text type classiﬁer to distinguish the text types occurring in our corpus. As the features of each text in our corpus are being annotated and there is a corpus annotated with text types in the L´ aicio-Web project  we will be able to better understand the correlations between text types and the others features for readability assessment in our project.
Therefore, we use the Active Learning Approach  for selecting new instances for annotation, so that the new instances are those that are most diﬃcult for our classiﬁer to label. Thus, we use the distance of texts from SVM separating hyperplanes as criteria for selecting instances for annotation. The closer an instance is from the separating hyperplanes, there is greater indecision in classifying that instance. Therefore, when we label this text manually, we believe we are helping the classiﬁer to better deﬁne the existing limits between classes.