This paper presents the description of a system which detects complex words. It solely uses information regarding the presence of a word in a prepared vocabulary list. The system outperforms multiple more advanced systems and is ranked fourth for the shared task, with minimal loss to the best system. F-score optimization guaranteed the first place in this measurement. Different features are considered and evaluated. Maximal bounds are predicted. The rule "the simplest methods give the best results" is confirmed.

Convolutional neural networks are modern models that are very efficient in many classification tasks. They were originally created for image processing purposes. Then some trials were performed to use them in different domains like natural language processing. The artificial intelligence systems (like humanoid robots) are very often based on embedded systems with constraints on memory, power consumption etc. Therefore convolutional neural network because of its memory capacity should be reduced to be mapped to given hardware. In this paper, results are presented of compressing the efficient convolutional neural networks for sentiment analysis. The main steps are quantization and pruning processes. The method responsible for mapping compressed network to FPGA and results of this implementation are presented. The described simulations showed that 5-bit width is enough to have no drop in accuracy from floating point version of the network. Additionally, significant memory footprint reduction was achieved (from 85% up to 93%).

No abstract

No abstract

No abstract