ITR: Domain-Independent Semantic Interpretation

这个项目的目标是建立一个领域无关的，语言无关的，经过训练的浅层语义解释器。 这是一个复杂、强大、广泛可用的工具，它将文本句子作为输入，并提供句子含义的“浅层”表示。 该研究解决了关键的科学问题，包括将表面形式与语义结构联系起来的自动提取语言特征的性质，特征和表示在语言中的鲁棒程度，以及监督和无监督学习技术的组合。 浅层语义解释器通过将句子分析为涉及谓词及其论元的命题，并通过语义角色集（施事，受事，工具，位置等）进行标记，从而在英语和汉语中工作。 该项目依赖于统计机器学习范式，包括从两个大型语义标记数据库中提取特征的监督方法（FrameNet和PropBank），以及无监督和轻度监督的方法，如图形模型聚类，协同训练和主动学习，包括各种复杂的语言特征。该项目的结果将允许浅层语义表示被纳入一个自然语言处理的所有关键应用，包括信息提取和机器翻译。 这项研究还将提供有关语言特征的重要知识，这些特征是意义的线索。 最后，该项目将为其他研究提供关键的公开数据库。其中包括FrameNet和PropBank数据库，这将是一个丰富的资源，为应用程序使用语义网和本体论，以及丰富的注释中文PropBank，对齐英语和中文语料库有用的机器翻译研究。

The goal of this project is to build a domain-independent, language-independent, statistically-trained shallow semantic interpreter. This is a sophisticated, robust, widely-available tool that takes text sentences as input and provides a `shallow' representation of the meaning of the sentence. The research addresses key scientific questions including the nature of automatically-extractable linguistic features that link surface form with semantic structure, the extent to which features and representations are robust across languages, and the combination of supervised and unsupervised learning techniques. The shallow semantic interpreter works in both English and Chinese by analyzing sentences into propositions involving predicates together with their arguments, labeled by sets of semantic roles (Agent, Patient, Instrument, Location, etc). The project relies on a statistical machine-learning paradigm, including supervised approaches in which features are extracted from two large semantically-labeled databases (FrameNet and PropBank), as well as unsupervised and lightly-supervised methods like clustering with graphical models, co-training, and active learning, including various sophisticated linguistic features.The results of the project will allow shallow semantic representations to be incorporated into an entire range of key Natural Language Processing applications, including information extraction and machine translation. The research will also provide important knowledge about the linguistic features that are cues to meaning. Finally, the project will provide key publicly-available databases for other research. These include the combined FrameNet and PropBank database, which will be a rich resource for applications using the Semantic Web and ontologies in general, as well as a richly-annotated Chinese PropBank, and aligned English and Chinese corpora useful for machine translation research.