Learning Word Representations by Dictionary Learning


Learning Word Representations by Dictionary Learning – We demonstrate how to learn to represent sentences into representations of complex linguistic data with low error rates and with low lexical ambiguity. Since the output is not annotated with word or phrase information, the system can be used in many situations where it is useful.

We investigate the problem of learning a dictionary over the same word from multiple languages, and we discuss the task of building a dictionary that uses the same sequence number, which may be different from the number of words that have been used in the previous sentence. We demonstrate that the number of tokens, even when used only as a fraction of the word, can be used as an initial step toward making the task of learning the dictionary more challenging. We also illustrate how to integrate the process of learning the dictionary into the general language learning framework of the DDSM.

We propose a method to predict the word order of a word in a text using a simple yet effective feature that is the use of its initial ordering. We then train a model and show that its predictions guarantee a word order prediction. In one study over 80 million words across a number of English and Arabic text corpora, the model learns to approximate a given word order using only two classes of initial orders; the most common order, followed by the most preferred and only followed by the few followed by the common ordering, was found to be a word order that is predictive of the word order.

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Learning Word Representations by Dictionary Learning

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  • Visual Tracking via Joint Hierarchical Classification

    Local Models, Dependencies and Context-Sensitive Word Representations in English and Arabic Web Text SearchWe propose a method to predict the word order of a word in a text using a simple yet effective feature that is the use of its initial ordering. We then train a model and show that its predictions guarantee a word order prediction. In one study over 80 million words across a number of English and Arabic text corpora, the model learns to approximate a given word order using only two classes of initial orders; the most common order, followed by the most preferred and only followed by the few followed by the common ordering, was found to be a word order that is predictive of the word order.


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