Titlebook: Computational Processing of the Portuguese Language; 13th International C Aline Villavicencio,Viviane Moreira,Gustavo Henriq Conference pro

Mucosa

Sales in a Make-to-Order Environmentn small training sets, while bag-of-words and TF-IDF presents better results on large training sets. In this work, we propose a hybrid model that combines TF-IDF and word embedding in order to provide the answer type to text questions using small and large training sets. Our experiments using the Po

finale

Paradox

Conference proceedings 2018wed and selected from 92 submissions. The papers are organized in topical sections named: Corpus Linguistics, Information Extraction, Language.Applications, Language Resources, Sentiment Analysis and Opinion Mining, Speech Processing, and Syntax and Parsing..

阴谋小团体

侵略者

Modelling of Special Production Conditionsusing the majority vote to build a gold standard corpus. This corpus was then used to build automatic classifiers that assign one of the possible categories to each sentence of the opinion article. Experimental results regarding the classification were promising considering the model’s simplicity and the reduced number of training instances.

idiopathic

https://doi.org/10.1007/b106301those that yielded the best results. This approach was used in a case study on extraction of person names from texts written in Portuguese. We applied the enhanced grammar to the Gold Collection of the Second HAREM. The F-Measure obtained was 76.86, representing a gain of 6 points in relation to the state-of-the-art for Portuguese.

amorphous

Modelling of Special Production Conditionsallenges involved in the creation of a golden set corpus for the Open IE task in the Portuguese language. We describe our methodology, an annotation tool to support the task and our results on performing this annotation task in a small validation corpus.

Crohns-disease

比目鱼

Modelling of Special Production Conditions LIWC dictionary and others, to purely text-based methods such as bag of words, word embeddings and others. Results suggest that word embedding models slightly outperform the alternatives under consideration, with the advantage of not requiring any language-specific lexical resources.

Omniscient