An Empirical Study of Neural Relation Graph Construction for Text Detection


An Empirical Study of Neural Relation Graph Construction for Text Detection – Conceptual logic provides a mechanism for reasoning about logic-like representations of language that can be used in a variety of applications, including data mining, human-computer interface and machine translation. Given basic logic, it can be easily inferred from the language, as we will show in this article, in the form of a logical model. We will not directly apply logic in the knowledge representation of language; instead, we will suggest a method of inference that is able to represent logic in a conceptual model that satisfies the need to understand and reason about logic. In this paper, we show that logic for logic networks can be inferred from the language. We can then extend this model to use logic for logical reasoning in languages that provide language like logic. Our experiments on real-world data collected from a database have shown that the model can be used within a logic-based reasoning system, as well as to learn and reason about logic.

We present an end-to-end model-based algorithm to encode and extract the semantic meanings of sentences. By extracting a semantic meaning from a sequence of sentences, we aim to capture the semantic structure in a graph and propose a method for learning from the set of sentences. Since the semantic meaning of sentences are expressed through a graph, we propose a novel, discriminative representation for these sentences using deep graph models (DNNs). Experiments using a novel dataset (Gibson Bayes dataset) and several supervised learning tasks, both in a real-world data set, have revealed that the proposed architecture achieves state-of-the-art accuracies on language segmentation.

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An Empirical Study of Neural Relation Graph Construction for Text Detection

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    Improving Recurrent Neural Networks with GraphsWe present an end-to-end model-based algorithm to encode and extract the semantic meanings of sentences. By extracting a semantic meaning from a sequence of sentences, we aim to capture the semantic structure in a graph and propose a method for learning from the set of sentences. Since the semantic meaning of sentences are expressed through a graph, we propose a novel, discriminative representation for these sentences using deep graph models (DNNs). Experiments using a novel dataset (Gibson Bayes dataset) and several supervised learning tasks, both in a real-world data set, have revealed that the proposed architecture achieves state-of-the-art accuracies on language segmentation.


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