UTMN at HAHA@IberLEF2019: Recognizing Humor in Spanish Tweets using Hard Parameter Sharing for Neural Networks

We explore different levels of information combination, from a low level combination (feature space combination), to mid-level com- binations (internal system

This article introduces the Echo State Queueing Network (ESQN), a new Reservoir Computing (RC) learning technique whose dynamics is inspired by the Random Neural Network (RNN), that

This method has been implemented using the USPTO Patent Database, with a comparative study of two Neural Networks: a Wide and Deep Neural Network and a Recurrent Neural Network,

The second combination adds a BLSTM- CTC in order to measure the ability of the BLSTM-CTC to learn and correct long term dependencies errors at a character level.. The third

Table 1: Clinical diagnosis of DR based on various feature types with different contributions to classification. One feature in each list is required for the equivalent DR grading.

The structure of the neural network used in the HAHA task by the Aspie96 team, although having been originally presented in [5] for the classification of Italian tweets, was not

Considering the advantages of linguistic features for capturing deep linguis- tics aspects of the language also the capability of RNN for learning deep rep- resentation and long

The second and the third approaches exploit recent advances in Natural Language Processing using a Hierarchical GRU-LSTM Neu- ral Network using word embeddings trained on Twitter