Collaborative Research: CISE-MSI: DP: IIS: Hybrid-Architecture Symbolic Parser with Neural Lexicon

如果一个话语符合说话者的心理语法，它就是合乎语法的。但语法上的话语可能是模棱两可的。歧义是为一种话语分配多种表征和解释的现象，是语言使用以实现有效交际的不良副产品；然而，由于心理语言表征具有层级结构，而语言外化由于感觉-运动限制而是线性的，所以歧义在语言运用中普遍存在。这个混合体系结构的符号分析器和神经词典系统(HASPNeL)判断给定的话语是否符合语法，并检测它在单词或句子级别是否存在歧义。HASPNeL也是遵循当前最简语法句法理论的人类语言计算认知模型，它必须满足可学习性、可演化性和普遍性条件。虽然符号-概率混合体系结构的优势已经在相关文献中得到了证明，但还没有任何基于该体系结构的类似系统。预计HASPNeL将影响教育和工业应用程序的发展(特别适用于没有足够可用数据的代表性较低的语言)，并进一步研究和改进人类语言认知模型和技术。HASPNeL的混合架构包括一个使用符号AI方法编码的特征统一解析器和结构生成器，以及一个用于构建丰富特征词典的机器学习标记器。带注释的合成语料库训练神经网络系统，该系统适当地识别和标记每个词汇项目，并估计语料库中每个类别的可能性。为了解释词汇歧义，不同类别、不同特征或不同含义的词条被赋予不同的词条。该系统仅分析语法话语，通过产生尽可能多的句法表示来识别歧义话语，并计算每个结构描述的可能性。HASPNeL能够通过对语法和词汇特征进行微小的参数调整来解释句法变化。该项目由MSI和既定的激励竞争研究计划(EPSCoR)联合资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

An utterance is grammatical if it conforms to the speaker's mental grammar. But grammatical utterances may be ambiguous. Ambiguity, the assignment of multiple representations and interpretations for one utterance, is an undesirable byproduct of language use for efficient communication; nevertheless, ambiguity is pervasive in language performance, since mental linguistic representations have a hierarchical structure, yet language externalization is linear due to sensory-motor constraints. This Hybrid-Architecture Symbolic Parser and Neural Lexicon system (HASPNeL) judges whether a given utterance is grammatical and detects whether it is ambiguous at the word or sentence level. HASPNeL is also a computational cognitive model of human language following current syntactic theory on Minimalist grammars, which must satisfy conditions of learnability, evolvability, and universality. Although the advantages of a hybrid symbolic-probabilistic architecture have been documented in relevant literature, there is not yet any comparable system based upon this architecture. HASPNeL is expected to impact the development of applications for education and industry (particularly applicable to underrepresented languages for which there is not enough available data), and to further research and advancement of human language cognitive models and technologies. HASPNeL's hybrid architecture comprises a feature-unification parser and structure generator, which is encoded using a symbolic AI approach, and a machine learning tagger that is used to construct a feature-enriched lexicon. An annotated synthetic corpus trains a neural network system that properly identifies and tags each lexical item and estimates the likelihood of each category within the corpus. To account for lexical ambiguity, tokens with different categories, features or meanings are assigned different entries. The system only parses grammatical utterances, recognizes ambiguous utterances by producing as many syntactic representations as there were possible interpretations, and calculates the likelihood for each structural description. HASPNeL is able to account for syntactic variation by minor parametric adjustments to grammatical and lexical features.This project is jointly funded by MSI and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.