By João Silva, Ricardo Ribeiro, Paulo Quaresma, André Adami, António Branco
This e-book constitutes the refereed court cases of the twelfth overseas 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 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 resources for Computational Processing of the Portuguese Language: 12th International Conference, PROPOR 2016, Tomar, Portugal, July 13-15, 2016, Proceedings
307–331. S. S. br Abstract. We deﬁne Multilingual Multi-Document Summarization (MMDS) as the process of identifying the main information of a cluster with (at least) two texts, one in the user’s language and one in a foreign language, and presenting it as a summary in the user’s language. Although it is a relevant task due to the increasing amount of on-line information in different languages, there are only baselines for (Brazilian) Portuguese, which apply machine-translation to obtain a monolingual input and superﬁcial features for sentence extraction.
Our experiments with fewer classes showed that, when joining classes 2 and 3, we achieved 65 % (+/−15) of accuracy, and by joining classes 4 and 5, we achieved 63 % (+/−11) of accuracy. By simultaneously joining class 2 with class 3 and 4 with 5, we reached the 74 % of accuracy achieved by the state of art. This division of grade levels better reﬂects the division into cycles indicated by the PCNs (1998). Automatic Classification of Text Complexity 21 Table 4. Confusion matrix of a 10-fold cross-validation experiment on our dataset.
2. Improving Question-Answering for Portuguese 29 Fig. 2. 1 Triple Extraction This module processes the contents of the corpus, picking each of the documents, selecting sentences and extracting triples. It includes multiple tasks, namely sentence splitting, phrase chunking, tokenization, POS tagging, lemmatization, dependency parsing, and NER. Except for lemmatization and dependency parsing, these tasks are done using the Apache OpenNLP toolkit 1 , with some minor tweaks for better addressing Portuguese, and with the models used for chunking and in being speciﬁcally created, as there was no available pre-built models.
Computational Processing of the Portuguese Language: 12th International Conference, PROPOR 2016, Tomar, Portugal, July 13-15, 2016, Proceedings by João Silva, Ricardo Ribeiro, Paulo Quaresma, André Adami, António Branco