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: 2219713
• 负责人: Sandiway Fong
• 金额: $ 20.56万
• 依托单位: University of Arizona
• 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=2219713&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.

如果一个话语符合说话者的心理语法，那么它就是合乎语法的。但语法上的表达可能是模棱两可的。歧义，即对一个话语分配多种表示和解释，是使用语言进行有效沟通的不良副产品。然而，由于心理语言表征具有层次结构，语言表现中普遍存在歧义，而由于感觉运动的限制，语言外化是线性的。这种混合架构符号解析器和神经词典系统（HASPNeL）可以判断给定的话语是否符合语法，并检测它是否在单词或句子级别上存在歧义。 HASPNeL也是遵循当前极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、可进化性和普遍性的条件。尽管混合符号概率架构的优点已在相关文献中得到记录，但目前还没有任何基于该架构的可比系统。 HASPNeL 预计将影响教育和工业应用程序的开发（特别适用于没有足够可用数据的代表性不足的语言），并进一步研究和推进人类语言认知模型和技术。 HASPNeL 的混合架构包括一个特征统一解析器和结构生成器（使用符号 AI 方法进行编码）和一个机器学习标记器（用于构建特征丰富的词典）。带注释的合成语料库训练神经网络系统，该系统可以正确识别和标记每个词汇项，并估计语料库中每个类别的可能性。为了解决词汇歧义，具有不同类别、特征或含义的标记被分配不同的条目。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 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 和刺激竞争性研究既定计划 (EPSCoR) 共同资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。