Complex words in context

辨别性词汇模型（DLM, Baayen et al., 2019; Chuang & Baayen, 2021）实现了心理词汇的计算理论。这个理论是针对单独考虑的单词而发展起来的，没有考虑任何上下文。然而，单词是如何说的，它们的意思是什么，取决于它们被使用的上下文。例如，英语中的cut可以表示用链锯、刀或剪刀进行的动作，荷兰语和普通话使用三个不同的动词来表示这些动作。英语中的cut有很多其他的含义，包括衍生词（cutter，一种船）、复合词（cutworm，一种蛾子幼虫）、词汇化表达（cut across）和习语（to cut classes）。Elman（2009）和Jackendoff & Audring（2020）指出，我们的词汇知识并不仅仅由简单和复杂的单词组成，而是由数以万计的多单词表达组成。此外，在一种语言中用一个词形复杂的词来表达的东西，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高维词汇/句法/语用空间中的点。这样一个空间，集成了分布语义、形态学和简单语法，需要开发算法来概念化，不仅包括屈曲特征（如数或时态），还包括句法角色（如施者和受者）和语用功能（如韩语和日语中的敬语）。至关重要的是，算法必须以这样一种方式建立，即具有不同语法或语用功能的实体被适当区分，同时保持词汇相似性。将DLM从孤立的单词扩展到上下文中的单词，不仅对基于用法的形态学的DLM形式化中的形式和词汇意义的表示方式提出了挑战，而且还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。增强的模型是本项目的核心成果，它将被应用于目前基于单词的DLM无法达到的广泛现象，如连读、外部变调、词形范式中的迂回用法、复合解释、大小写屈折的解释、固定短语和习语、词汇意义的上下文依赖以及敬语的形态。重要的是，由于DLM将分布语义学纳入形态学理论，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。

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.