Many words are ambiguous in terms of their part of speech (POS). However, when a word appears in a text, this ambiguity is generally much reduced. Disambiguating POS involves using context to reduce the number of POS associated with words, and is one of the main challenges of lexical tagging. The problem of labeling words by POS frequently arises in natural language processing, for example for spelling correction, grammar or style checking, expression recognition, text-to-speech conversion, text corpus analysis, etc. Lexical tagging systems are thus useful as an initial component of many natural language processing systems. A number of recent lexical tagging systems produce multiple solutions when the text is lexically ambiguous or the uniquely correct solution cannot be found. These contributions aim to guarantee a zero silence rate: the correct tag(s) for a word must never be discarded. This objective is unrealistic for systems that tag each word uniquely. This article concerns a lexical disambiguation method adapted to the objective of a zero silence rate and implemented in Silberztein's INTEX system (1993). We present here a formal description of this method. We show that to verify a local disambiguation grammar in this framework, it is not sufficient to consider the transducer paths separately: one needs to verify their interactions. Similarly, if a combination of multiple transducers is used, the result cannot be predicted by considering them in isolation. Furthermore, when examining the initial labeling of a text as produced by INTEX, ideas for disambiguation rules come spontaneously, but grammatical intuitions may turn out to be inaccurate, often due to an unforeseen construction or ambiguity. If a zero silence rate is targeted, local grammars must be carefully tested. This is where a detailed specification of what a grammar will do once applied to texts would be necessary.

翻译：许多词汇在词性上存在歧义。然而，当词汇出现在文本中时，这种歧义通常大幅减少。词性消解涉及利用上下文来减少词汇关联的词性数量，是词汇标注的主要挑战之一。词性标注问题在自然语言处理中频繁出现，例如拼写校正、语法或风格检查、表达式识别、文本转语音转换、文本语料库分析等。因此，词汇标注系统作为许多自然语言处理系统的初始组件具有重要价值。近年来，当文本存在词汇歧义或无法确定唯一正确解时，部分词汇标注系统会生成多个解决方案。这些研究旨在保证零静默率：词汇的正确标签绝不能丢失。对于为每个词汇提供唯一标注的系统而言，这一目标并不现实。本文探讨一种适用于零静默率目标的词汇消解方法，该方法已在Silberztein的INTEX系统（1993）中实现。本文对该方法进行形式化描述。研究表明，在此框架下验证局部消解语法时，仅单独考虑转换器路径是不够的：必须验证其交互作用。同理，若使用多个转换器的组合，其结果无法通过孤立分析来预测。此外，在检查INTEX生成的文本初始标注时，消解规则的构思会自然浮现，但语法直觉可能因未预见的结构或歧义而产生偏差。若以零静默率为目标，必须对局部语法进行严格测试。这就需要详细规范语法应用于文本时的具体行为。