语音算法在微文本规范化中的性能研究
On the performance of phonetic algorithms in microtext normalization
AI总结：
本研究通过实验比较多种英语语音算法，旨在为微文本规范化中的候选生成找到最优算法，以平衡候选集大小与覆盖率，提升规范化系统整体性能。
摘要：
用户在微博社交网络上发布的生成内容构成了宝贵的信息来源。然而，微文本通常偏离语言的标准词汇和语法规则，这使得传统智能系统难以处理。作为应对措施，微文本规范化旨在将那些非标准微文本转换为标准且书写规范的文本，作为预处理步骤，从而使传统方法能够继续其常规处理。鉴于语音现象在非标准文本形成中的重要性，规范化器知识库的一个关键要素将是编码这些现象的语音规则，这些规则可以在所谓的语音算法中找到。在本研究中，我们对英语的各种语音算法进行了实验。本研究的目的是确定在微文本规范化的候选生成背景下最佳的语音算法。换言之，我们旨在找到那些以需要规范化的非标准术语为输入，能够输出尽可能小的规范化候选集且仍包含相应目标标准词的算法。正如将要说明的，语音算法的选择将很大程度上取决于我们通常在微文本规范化流程末尾发现的候选选择机制的能力。该机制在足够大的候选集中做出正确选择的速度越快，我们就越能牺牲语音算法的精确性以换取覆盖率，从而提高规范化系统的整体性能。关键词：微文本规范化；语音算法；模糊匹配；推特；短信
英文摘要：
User-generated content published on microblogging social networks constitutes a priceless source of information. However, microtexts usually deviate from the standard lexical and grammatical rules of the language, thus making its processing by traditional intelligent systems very difficult. As an answer, microtext normalization consists in transforming those non-standard microtexts into standard well-written texts as a preprocessing step, allowing traditional approaches to continue with their usual processing. Given the importance of phonetic phenomena in non-standard text formation, an essential element of the knowledge base of a normalizer would be the phonetic rules that encode these phenomena, which can be found in the so-called phonetic algorithms. In this work we experiment with a wide range of phonetic algorithms for the English language. The aim of this study is to determine the best phonetic algorithms within the context of candidate generation for microtext normalization. In other words, we intend to find those algorithms that taking as input non-standard terms to be normalized allow us to obtain as output the smallest possible sets of normalization candidates which still contain the corresponding target standard words. As it will be stated, the choice of the phonetic algorithm will depend heavily on the capabilities of the candidate selection mechanism which we usually find at the end of a microtext normalization pipeline. The faster it can make the right choices among big enough sets of candidates, the more we can sacrifice on the precision of the phonetic algorithms in favour of coverage in order to increase the overall performance of the normalization system. KEYWORDS: microtext normalization; phonetic algorithm; fuzzy matching; Twitter; texting
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