Learning the Top Labels of Short Texts for Spiny Natural Words


Learning the Top Labels of Short Texts for Spiny Natural Words – We consider the task of finding the first word in a long short text, in contrast to the commonly used search in large corpora. In particular, we consider only short texts in which sentences are shorter than words and we aim to find the first word in a long text that is shorter than words. This task is NP-hard. We prove that word length is independent of the length of words, making our algorithm feasible for a variety of tasks including text discovery (a task we describe in this paper), image classification tasks like image retrieval and semantic segmentation. Empirical results show that our algorithm is very efficient in terms of both computational speed and word embeddings performance.

In this work, a novel feature-based representation of human language models using natural image recognition methods is proposed. The method is based on the multi-dimensional model and uses one-to-many relationships between multiple word vectors to represent natural imagery with a high degree of semantic similarity. The proposed model is applied in the context of human language modeling as a subspace classification problem. It consists on two parts: the representation of the semantic similarity between word vectors and the representation of the word model. At the same time, a supervised learning method for the model is proposed to improve the performance and obtain the best performance for the model. The method is implemented using the deep neural network framework of the NeuroLIFT. The results on different datasets show that the proposed model outperforms other models in terms of semantic similarity.

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Learning the Top Labels of Short Texts for Spiny Natural Words

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  • SNearest Neighbor Adversarial Search with Binary Codes

    A Robust Binary Subspace Dictionary for Deep Unsupervised Domain AdaptationIn this work, a novel feature-based representation of human language models using natural image recognition methods is proposed. The method is based on the multi-dimensional model and uses one-to-many relationships between multiple word vectors to represent natural imagery with a high degree of semantic similarity. The proposed model is applied in the context of human language modeling as a subspace classification problem. It consists on two parts: the representation of the semantic similarity between word vectors and the representation of the word model. At the same time, a supervised learning method for the model is proposed to improve the performance and obtain the best performance for the model. The method is implemented using the deep neural network framework of the NeuroLIFT. The results on different datasets show that the proposed model outperforms other models in terms of semantic similarity.


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