Natural Language Processing at the Sub-Word Level

子词级别的自然语言处理

• 批准号: 261284-2012
• 负责人: Kondrak, Grzegorz
• 金额: $ 1.24万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=570990
• 关键词: Natural; Language; Processing; Sub; Word

Processing language involves processing words. However, words are not indivisible abstract atoms - they are made of smaller units, such as morphemes, syllables, letters, and phonemes. In computational linguistics, words are analyzed on distinct levels: phonetic, phonological, orthographic, morphological, etc. These levels of representation underlie such important tasks as morphological parsing, speech synthesis and recognition, spell-checking, and stemming. Since different levels are strongly inter-related, the understanding of the interactions between them is crucial to advancing the state of the art in word-oriented applications. My objective is to investigate such interactions, and develop algorithms for alignment and conversion between levels. In particular, I will focus on the tasks of grapheme-to-phoneme conversion and transliteration. My general approach to the problem will be to incorporate linguistic knowledge into advanced machine-learning techniques, which, given sufficient training data, substantially outperform rule-based approaches. However, the latter often achieve impressive accuracy without any need for training data. They also tend to perform well across different domains. I will explore the ways of combining the two paradigms, by exploiting linguistic understanding for guiding and informing machine learning approaches, as well as for pre- and post-processing their training data. The long term goals of this research programme are acquiring deeper understanding of the dependencies between various representations, and leveraging the novel insights in order to advance the state of the art in natural language processing. I am confident that the implementation of the planned projects will result in successful applications in other areas of natural language processing.

处理语言涉及处理单词。然而，文字并不是不可分割的抽象原子—— 它们由较小的单位组成，例如语素、音节、字母和音素。在计算方面 在语言学中，单词在不同的层面上进行分析：语音、音系、拼写、 这些表示级别是形态学等重要任务的基础 语法分析、语音合成和识别、拼写检查和词干提取。由于级别不同 相互关联性很强，了解它们之间的相互作用对于推进 面向单词的应用程序的最新技术。我的目标是调查这种相互作用，并且 开发级别之间的对齐和转换算法。我将特别关注 字素到音素的转换和音译任务。 我解决这个问题的一般方法是将语言知识融入高级 机器学习技术，如果有足够的训练数据，其性能将大大优于 基于规则的方法。然而，后者通常无需任何操作即可达到令人印象深刻的准确性 训练数据。他们也往往在不同领域表现良好。我将探索以下方法 结合两种范式，利用语言理解来指导和通知机器 学习方法，以及训练数据的预处理和后处理。 该研究计划的长期目标是更深入地了解依赖性 各种表征之间的关系，并利用新颖的见解来推进状态 自然语言处理的艺术。我对计划项目的实施充满信心 将在自然语言处理的其他领域取得成功的应用。