In this paper we present an alternative method to Tiered Tagging, based on local optimizations with Neural Networks and we show how, by properly encoding the input sequence in a general Neural Network architecture, we achieve results similar to the Tiered Tagging methodology, significantly faster and without requiring extensive linguistic knowledge as implied by the previously mentioned method.

In this article, we propose an alternative solution based on local optimizations with feed-forward neural networks .

2 Large tagset part-of—speech tagging with feed-forward neural networks

A neural network is a reasonable method to address these pitfalls.

However, modeling SMT with a neural network is not trivial, especially when taking the decoding efficiency into consideration.

In this paper, we propose a variant of a neural network , i.e.

A neural network (Bishop, 1995) is a reasonable method to overcome the above shortcomings.

In the search procedure, frequent computation of the model score is needed for the search heuristic function, which will be challenged by the decoding efficiency for the neural network based translation model.

In this paper, we propose a variant of neural networks , i.e.

Instead, we introduce a Compositional Vector Grammar (CVG), which combines PCFGs with a syntactically untied recursive neural network that learns syntactico-semantic, compositional vector representations.

The vectors for nonterminals are computed via a new type of recursive neural network which is conditioned on syntactic categories from a PCFG.

l. CVGs combine the advantages of standard probabilistic context free grammars (PCFG) with those of recursive neural networks (RNNs).

This requires the composition function to be extremely powerful, since it has to combine phrases with different syntactic head words, and it is hard to optimize since the parameters form a very deep neural network .

In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network ), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011).

The lookup process is called a lookup layer LT , which is usually the first layer after the input layer in neural network .

Multilayer neural networks are trained with the standard back propagation algorithm (LeCun, 1985).

Techniques such as layerwise pre-training(Bengio et al., 2007) and many tricks(LeCun et al., 1998) have been developed to train better neural networks .

Recent years research communities have seen a strong resurgent interest in modeling with deep (multilayer) neural networks .

For speech recognition, (Dahl et al., 2012) proposed context-dependent neural network with large vocabulary, which achieved 16.0% relative error reduction.

(Collobert et al., 2011) and (Socher et al., 2011) further apply Recursive Neural Networks to address the structural prediction tasks such as tagging and parsing, and (Socher et al., 2012) explores the compositional aspect of word representations.

(Seide et al., 2011) and (Dahl et al., 2012) apply Context-Dependent Deep Neural Network with HMM (CD-DNN-HMM) to speech recognition task, which significantly outperforms traditional models.

(Bengio et al., 2006) proposed to use multilayer neural network for language modeling task.

We propose a novel entity disambiguation model, based on Deep Neural Network (DNN).

Deep neural networks (Hinton et al., 2006; Bengio et al., 2007) are built in a hierarchical manner, and allow us to compare context and entity at some higher level abstraction; while at lower levels, general concepts are shared across entities, resulting in compact models.

BTS is a variant of the general backpropagation algorithm for structured neural network .