Complex words in context

上下文中的复杂单词

• 批准号: 527671319
• 负责人: Professor Dr. Harald Baayen
• 金额: --
• 依托单位: Arbeitsbereich Quantitative Linguistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/527671319?language=en
• 关键词: Complex; words; context

The Discriminative Lexicon Model (DLM, Baayen et al., 2019; Chuang & Baayen, 2021) implements a computational theory of the mental lexicon. This theory has been developed for words considered by themselves, without taking any context into account. However, how words are spoken, and what they mean, depends on the context in which they are used. For instance, English cut can denote actions car- ried out with chainsaws, knives, or scissors, actions for which Dutch and Mandarin use three different verbs. English cut displays a wide range of other meanings, across derivations (cutter, a type of ship), compounds (cutworm, a moth larva), lexicalized expressions (cut across), and idioms (to cut classes). Elman (2009) and Jackendoff & Audring (2020) have pointed out that our lexical knowledge does not consist of just simple and complex words, but of tens of thousands of multi-word expressions. Furthermore, what in one language is expressed with a single morphologically complex word, may require a phrase in other languages. Central to the present project proposal is the hypothesis that the meanings of small utterances can be represented as points in a high-dimensional lexical/syntactic/pragmatic space. Such a space, which integrates distributional semantics with morphology and simple syntax, requires the development of algorithms for the conceptualization not only of inflectional features such as number or tense, but also of syntactic roles such as agent and patient, and pragmatic functions such as honorifics (as found in Korean and Japanese). Crucially, the algorithms have to be set up in such a way that entities with different syntactic or pragmatic functions are properly distinguished, while maintaining lexical similarities. Extending the DLM from isolated words to words in context not only poses challenges for the way in which form and lexical meaning are to be represented within the DLM formalization of usage-based morphology, but also addresses fundamental questions in linguistic theory about the relation between form and meaning. The enhanced model that is the central deliverable of this project will be applied to a wide range of phenomena that are currently out of reach of the word-based DLM, such as liaison, external sandhi, periphrasis in morphological paradigms, compound interpretation, the interpretation of case inflection, fixed phrases and idioms, the context-dependence of lexical meaning and the morphology of honorifics. Importantly, because the DLM incorporates distributional semantics into morphological theory, it becomes possible to study in detail how subtle differences in meaning modulate speech production and auditory comprehension.

区别性词典模型（DLM，Baayen等人，2019; Chuang & Baayen，2021）实现了心理词典的计算理论。这一理论是针对单词本身而发展起来的，没有考虑任何上下文。然而，词语的表达方式以及它们的含义取决于它们使用的上下文。例如，英语cut可以表示用链锯、刀或剪刀进行的动作，荷兰语和汉语中使用三个不同的动词。英语cut还有很多其他含义，包括派生词（cutter，一种船）、复合词（cutworm，一种蛾幼虫）、词汇化表达（cut across）和习语（cut classes）。Elman（2009）和Jackendoff & Audring（2020）指出，我们的词汇知识不仅仅由简单和复杂的单词组成，而是由成千上万的多词表达组成。此外，在一种语言中用一个形态复杂的词表达的东西，在其他语言中可能需要一个短语。本项目建议的核心是假设，小话语的意义可以表示为一个高维的词汇/句法/语用空间中的点。这样一个空间，它集成了分布语义与形态和简单的语法，需要发展的算法不仅概念化的屈折变化的功能，如数量或紧张，但也的句法角色，如代理人和病人，和语用功能，如honorphics（如韩语和日语）。至关重要的是，算法必须以这样一种方式建立，即具有不同句法或语用功能的实体被正确区分，同时保持词汇相似性。将DLM从孤立词扩展到语境中的词，不仅对基于用法的形态学DLM形式化中形式和词汇意义的表示方式提出了挑战，而且解决了语言学理论中关于形式和意义之间关系的基本问题。增强模型是本项目的核心成果，它将被应用于目前基于词的DLM无法触及的广泛现象，如连读、外部连读、形态范式中的迂回、复合解释、格变化的解释、固定短语和成语、词汇意义的上下文依赖性和音位词的形态。重要的是，由于DLM将分布语义纳入形态学理论，因此可以详细研究意义的细微差异如何调节语音产生和听觉理解。