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Neural Network Approaches for Noisy Language Modeling

机译：噪声语言建模的神经网络方法

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摘要

Text entry from people is not only grammatical and distinct, but also noisy. For example, a user's typing stream contains all the information about the user's interaction with computer using a QWERTY keyboard, which may include the user's typing mistakes as well as specific vocabulary, typing habit, and typing performance. In particular, these features are obvious in disabled users' typing streams. This paper proposes a new concept called noisy language modeling by further developing information theory and applies neural networks to one of its specific application-typing stream. This paper experimentally uses a neural network approach to analyze the disabled users' typing streams both in general and specific ways to identify their typing behaviors and subsequently, to make typing predictions and typing corrections. In this paper, a focused time-delay neural network (FTDNN) language model, a time gap model, a prediction model based on time gap, and a probabilistic neural network model (PNN) are developed. A 38% first hitting rate (HR) and a 53% first three HR in symbol prediction are obtained based on the analysis of a user's typing history through the FTDNN language modeling, while the modeling results using the time gap prediction model and the PNN model demonstrate that the correction rates lie predominantly in between 65% and 90% with the current testing samples, and 70% of all test scores above basic correction rates, respectively. The modeling process demonstrates that a neural network is a suitable and robust language modeling tool to analyze the noisy language stream. The research also paves the way for practical application development in areas such as informational analysis, text prediction, and error correction by providing a theoretical basis of neural network approaches for noisy language modeling.
机译：人们输入的文本不仅语法和独特，而且嘈杂。例如，用户的键入流包含有关用户使用QWERTY键盘与计算机进行交互的所有信息，其中可能包括用户的键入错误以及特定的词汇，键入习惯和键入性能。尤其是，这些功能在残障用户的输入流中很明显。通过进一步发展信息理论，本文提出了一种称为“噪声语言建模”的新概念，并将神经网络应用于其特定的应用程序类型流中。本文通过实验使用神经网络方法，以一般和特定方式分析了残疾用户的打字流，以识别他们的打字行为，然后进行打字预测和打字纠正。本文开发了一种聚焦时延神经网络（FTDNN）语言模型，一个时空模型，一个基于时空的预测模型以及一个概率神经网络模型（PNN）。基于通过FTDNN语言建模对用户的键入历史进行分析，获得了38％的首次命中率（HR）和53％的前三个HR（符号预测），而建模结果则使用了时间间隔预测模型和PNN模型证明当前测试样本的校正率主要在65％至90％之间，所有测试分数的70％分别高于基本校正率。建模过程表明，神经网络是分析嘈杂的语言流的合适且健壮的语言建模工具。这项研究还通过提供用于噪声语言建模的神经网络方法的理论基础，为信息分析，文本预测和错误纠正等领域的实际应用开发铺平了道路。

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