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Long Short-Term Memory (LSTM) is a specific recurrent neu-ral network (RNN) architecture that was designed to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we explore LSTM RNN architectures for large scale acoustic modeling in speech recognition. We recently showed that LSTM RNNs are more(More)
In this paper, we propose a set of language resources for building Turkish language processing applications. Specifically, we present a finite-state implementation of a morphological parser, an averaged perceptron-based morphological disambiguator, and compilation of a web corpus. Turkish is an agglutinative language with a highly productive inflectional(More)
Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture that has been designed to address the vanishing and exploding gradient problems of conventional RNNs. Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences. They have been successfully used for sequence labeling and sequence(More)
Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) have shown improvements over Deep Neural Networks (DNNs) across a wide variety of speech recognition tasks. CNNs, LSTMs and DNNs are complementary in their modeling capabilities, as CNNs are good at reducing frequency variations, LSTMs are good at temporal modeling, and DNNs are(More)
This paper describes the application of the perceptron algorithm to the morphological disambiguation of Turkish text. Turkish has a productive derivational morphology. Due to the ambiguity caused by complex morphology, a word may have multiple morphological parses, each with a different stem or sequence of morphemes. The methodology employed is based on(More)
We have recently shown that deep Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) outperform feed forward deep neural networks (DNNs) as acoustic models for speech recognition. More recently, we have shown that the performance of sequence trained context dependent (CD) hidden Markov model (HMM) acoustic models using such LSTM RNNs can be(More)
—This paper introduces two complementary language modeling approaches for morphologically rich languages aiming to alleviate out-of-vocabulary (OOV) word problem and to exploit morphology as a knowledge source. The first model, morpholexical language model, is a generative-gram model, where modeling units are lexical-grammatical morphemes instead of(More)
We present a set of language resources and tools—a morphological parser, a morphological disambiguator, and a text corpus—for exploiting Turkish morphology in natural language processing applications. The morphological parser is a state-of-the-art finite-state transducer-based implementation of Turkish morphology. The disambiguator is based on the averaged(More)