In this paper, we propose two novel approaches based on neural networks for building predictors of distributed syntactic structures. The first model is based on ...

Dec 11, 2015 · The first approach is based on a multi-layer perceptron to learn how to map vectors representing sentences into embedded syntactic structures.

This paper proposes two approaches for predicting the embedded syntactic structures of sentences using recurrent neural networks with long short-term memory ...

We propose two approaches for predicting the embedded syntactic structures. The first approach is based on a multi-layer perceptron to learn how to map vectors ...

RSD-LSTM employs a convolutional neural network to compute the relative syntactic distance between sentences to represent the degree of dependency between words ...

This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application ...

Apr 1, 2024 · Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models ...

In a neural network language model, words from the training data are mapped to learned vector embeddings, and sequences of those embeddings are fed into a ...

One such approach is to use recurrent neural networks (RNNs). An RNN processes the sentence from left to right, maintaining a single vector ht , the so-called ...

Aug 7, 2022 · Abstract. Neural network-based embeddings have been the mainstream approach for creating a vector representation of the text to capture ...