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

合作研究：CISE-MSI：DP：IIS：带有神经词典的混合架构符号解析器

• 批准号: 2219712
• 负责人: Hilton Alers-Valentin
• 金额: $ 39.44万
• 依托单位: University of Puerto Rico Mayaguez
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2219712&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; DP

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.

话语是语法的，如果它符合说话者的精神语法。但是语法话语可能是模棱两可的。歧义是一种语言的多种表示和解释的分配，是语言使用的不良副产品，以进行有效的交流。然而，由于精神语言表征具有层次结构，因此歧义性在语言表现上普遍存在，但是由于感觉运动的限制，语言外部化是线性的。这种混合体系结构的象征性解析器和神经词典系统（HASPNEL）判断给定的话语是语法上的，并检测到在单词还是句子级别上是模棱两可的。 Haspnel也是人类语言的计算认知模型，遵循有关最低限度语法的当前句法理论，该理论必须满足可学习性，可发展性和普遍性的条件。尽管在相关文献中已经记录了混合象征性 - 稳定体系结构的优势，但基于此体系结构尚无任何可比较的系统。预计Haspnel将影响教育和行业应用程序的发展（尤其适用于没有足够可用数据的代表性不足的语言），并进一步研究和进步人类语言认知模型和技术。 Haspnel的混合体系结构包括一个功能统一解析器和结构生成器，该解析器和结构生成器使用符号AI方法编码，以及用于构造功能增强词典的机器学习标签器。带注释的合成语料库训练一个神经网络系统，该系统正确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。为了说明词汇歧义，具有不同类别，功能或含义的令牌分配了不同的条目。该系统仅解析语法话语，通过产生与可能的解释一样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 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.