scholarly journals Structured semantic knowledge can emerge automatically from predicting word sequences in child-directed speech

2017 ◽  
Author(s):  
Philip Huebner ◽  
Jon Willits
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Semantic Knowledge ◽  
Learning System ◽  
Semantic Features ◽  
Learning Approaches ◽  
Learning Mechanisms ◽  
Distributional Learning ◽  
Structured Knowledge

Previous research has suggested that distributional learning mechanisms may contribute to the acquisition of semantic knowledge. However, distributional learning mechanisms, statistical learning, and contemporary “deep learning” approaches have been criticized for being incapable of learning the kind of abstract and structured knowledge that many think is required for acquisition of semantic knowledge. In this paper, we show that recurrent neural networks, trained on noisy naturalistic speech to children, do in fact learn what appears to be abstract and structured knowledge. We trained two types of recurrent neural networks (Simple Recurrent Network, and Long Short-Term Memory) to predict word sequences in a 5-million-word corpus of speech directed to children ages 0 to 3 years old, and assessed what semantic knowledge they acquired. We found that learned internal representations are encoding various abstract grammatical and semantic features that are useful for predicting word sequences. Assessing the organization of semantic knowledge in terms of the similarity structure, we found evidence of emergent categorical and hierarchical structure in both models. We found that the LSTM and SRN are both learning very similar kinds of representations, but the LSTM achieved higher levels of performance on a quantitative evaluation. We also trained a non-recurrent neural network, Skip-gram, on the same input to compare our results to the state-of-the-art in machine learning. We found that Skip-gram achieves relatively similar performance to the LSTM, but is representing words more in terms of thematic compared to taxonomic relations, and we provide reasons why this might be the case. Our findings show that a learning system that derives abstract, distributed representations for the purpose of predicting sequential dependencies in naturalistic language may provide insight into emergence of many properties of the developing semantic system.

Fake News Detection Using Recurrent Neural Networks and Distributed Representations for the Portuguese Language

2021 ◽  
Author(s):  
Guilherme Zanini Moreira ◽  
Marcelo Romero ◽  
Manassés Ribeiro
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Classification Performance ◽  
Fake News ◽  
Distributed Representations ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Media Channels ◽  
Damage Rule

After the advent of Web, the number of people who abandoned traditional media channels and started receiving news only through social media has increased. However, this caused an increase of the spread of fake news due to the ease of sharing information. The consequences are various, with one of the main ones being the possible attempts to manipulate public opinion for elections or promotion of movements that can damage rule of law or the institutions that represent it. The objective of this work is to perform fake news detection using Distributed Representations and Recurrent Neural Networks (RNNs). Although fake news detection using RNNs has been already explored in the literature, there is little research on the processing of texts in Portuguese language, which is the focus of this work. For this purpose, distributed representations from texts are generated with three different algorithms (fastText, GloVe and word2vec) and used as input features for a Long Short-term Memory Network (LSTM). The approach is evaluated using a publicly available labelled news dataset. The proposed approach shows promising results for all the three distributed representation methods for feature extraction, with the combination word2vec+LSTM providing the best results. The results of the proposed approach shows a better classification performance when compared to simple architectures, while similar results are obtained when the approach is compared to deeper architectures or more complex methods.

Convolutional Nonlinear Differential Recurrent Neural Networks for Crowd Scene Understanding

2018 ◽  
Vol 12 (04) ◽  
pp. 481-500 ◽  
Author(s):  
Naifan Zhuang ◽  
The Duc Kieu ◽  
Jun Ye ◽  
Kien A. Hua
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Scene Understanding ◽  
Image Data ◽  
High Density ◽  
Temporal Information ◽  
Deep Model ◽  
End To End ◽  
The Individual

With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility, and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, convolutional nonlinear differential recurrent neural networks (CNDRNNs), for crowd scene understanding. CNDRNNs consist of GoogleNet Inception V3 convolutional neural networks (CNNs) and nonlinear differential recurrent neural networks (RNNs). Different from traditional non-end-to-end solutions which separate the steps of feature extraction and parameter learning, CNDRNN utilizes a unified deep model to optimize the parameters of CNN and RNN hand in hand. It thus has the potential of generating a more harmonious model. The proposed architecture takes sequential raw image data as input, and does not rely on tracklet or trajectory detection. It thus has clear advantages over the traditional flow-based and trajectory-based methods, especially in challenging crowd scenarios of high density and low mobility. Taking advantage of CNN and RNN, CNDRNN can effectively analyze the crowd semantics. Specifically, CNN is good at modeling the semantic crowd scene information. On the other hand, nonlinear differential RNN models the motion information. The individual and increasing orders of derivative of states (DoS) in differential RNN can progressively build up the ability of the long short-term memory (LSTM) gates to detect different levels of salient dynamical patterns in deeper stacked layers modeling higher orders of DoS. Lastly, existing LSTM-based crowd scene solutions explore deep temporal information and are claimed to be “deep in time.” Our proposed method CNDRNN, however, models the spatial and temporal information in a unified architecture and achieves “deep in space and time.” Extensive performance studies on the Violent-Flows, CUHK Crowd, and NUS-HGA datasets show that the proposed technique significantly outperforms state-of-the-art methods.

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