论文标题
隐藏的马尔可夫连锁店，熵向前返回和言论的一部分标记
Hidden Markov Chains, Entropic Forward-Backward, and Part-Of-Speech Tagging
论文作者
论文摘要
在自然语言处理（NLP）问题中，考虑到观察的特征（也称为特征）的能力至关重要。与经典前进概率相关的隐藏马尔可夫链（HMC）模型无法处理除独立条件以外的任何大小的前缀或后缀之类的任意功能。二十年来，该默认值一直鼓励开发其他顺序模型，从最大熵马尔可夫模型（MEMM）开始，该模型优雅地整合了任意特征。更普遍地，它导致HMC忽略了NLP。在本文中，我们表明问题不是由于HMC本身，而是由于计算其恢复算法的方式。我们提出了一种使用原始熵向后和后退（EFB）概率计算基于HMC的修复体的新方法。我们的方法允许以与MEMM框架相同的方式考虑HMC框架中的功能。我们说明了使用EFB在言论部分标记中使用HMC的效率，显示了其优越性比基于MEMM的恢复的优势。作为一个角度，我们还指定了具有EFB的HMC如何作为复发性神经网络的替代方法，以使用深度体系结构处理顺序数据。
The ability to take into account the characteristics - also called features - of observations is essential in Natural Language Processing (NLP) problems. Hidden Markov Chain (HMC) model associated with classic Forward-Backward probabilities cannot handle arbitrary features like prefixes or suffixes of any size, except with an independence condition. For twenty years, this default has encouraged the development of other sequential models, starting with the Maximum Entropy Markov Model (MEMM), which elegantly integrates arbitrary features. More generally, it led to neglect HMC for NLP. In this paper, we show that the problem is not due to HMC itself, but to the way its restoration algorithms are computed. We present a new way of computing HMC based restorations using original Entropic Forward and Entropic Backward (EFB) probabilities. Our method allows taking into account features in the HMC framework in the same way as in the MEMM framework. We illustrate the efficiency of HMC using EFB in Part-Of-Speech Tagging, showing its superiority over MEMM based restoration. We also specify, as a perspective, how HMCs with EFB might appear as an alternative to Recurrent Neural Networks to treat sequential data with a deep architecture.
