By João Silva, Ricardo Ribeiro, Paulo Quaresma, André Adami, António Branco
This booklet constitutes the refereed lawsuits 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 offered during this quantity have been rigorously reviewed and chosen from fifty two submissions. The papers are equipped in topical sections named: language functions, language processing, and language resources.
Read Online 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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Additional info for Computational Processing of the Portuguese Language: 12th International Conference, PROPOR 2016, Tomar, Portugal, July 13-15, 2016, Proceedings
Keywords: Question Answering · Open information extraction · Triple extraction · Portuguese 1 Introduction The quest for information is a quintessential human endeavour. And as soon as computers came into play, they immediately started to be used for storing and retrieving information, most of which in the form of natural language. Not long after, there were attempts to use computers in tasks related to natural language processing (NLP), trying to make sense of all the data described using natural language, which keeps increasing by the day.
This work makes the assumption that such knowledge allows to take into account information from all source texts in their original language to perform content selection, producing better summaries both in terms of informativeness, since the selection is based on salient concepts, and linguistic quality, because only summary sentences in a foreign language require to be translated. Particularly, we report our investigation on 2 methods for summarizing a bilingual cluster (Portuguese and English) to produce an extract in Portuguese.
Roark and Fisher  take as input a cluster of some translated texts to English, some English spoken language texts, and some English texts. The method ranks the sentences from all the texts based on 9 superﬁcial features and sets a high preference for English sentences when selecting them from the ranking to compose the English extract. Of the nine features, 8 are different versions of tf-idf, log-likelihood ratio, and log-odds ratio lexical measures, and the ninth is the position of the sentence in the text.