Compositional POS Induction via Neural Networks


Compositional POS Induction via Neural Networks – In this paper, we model a general purpose neural network for POS induction using a single set of sentences. This network is composed of multiple steps to the training stage. We show that the two-step model can be decomposed into two sub-modalities — one for the training stage and one for the induction stage. To overcome the inconsistency in the two-step model, we first use a linear-time recurrent neural network model to compute the sentence representations. This procedure is trained from a two-stage framework, where each sentence is extracted directly from the previous one. We show that the output of the neural network is a novel POS induction model and the resulting sequence can be decomposed into a large number of sentences, each of which contains an extra sentence that was extracted from a previous sentence. We apply the proposed method to an experiment for POS induction from a sentence generation task. Our experiments show that our algorithm significantly outperforms the state-of-the-art results in this task.

We present an efficient algorithm for the semi-supervised learning (SSL) problem of estimating the value of an unknown quantity. Our algorithm is a simple and effective algorithm to solve the first stage, that requires no machine-learning or domain modeling involved. The algorithm can be efficiently compared with most existing algorithms for both semi-supervised and supervised learning tasks. Besides, we show that this algorithm is very easy to implement and work on.

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Compositional POS Induction via Neural Networks

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  • Machine Learning for Cognitive Tasks: The State of the Art

    MIDA: Multiple Imputation Models and Acceleration of InferenceWe present an efficient algorithm for the semi-supervised learning (SSL) problem of estimating the value of an unknown quantity. Our algorithm is a simple and effective algorithm to solve the first stage, that requires no machine-learning or domain modeling involved. The algorithm can be efficiently compared with most existing algorithms for both semi-supervised and supervised learning tasks. Besides, we show that this algorithm is very easy to implement and work on.


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