ITR: Domain-Independent Semantic Interpretation

该项目的目标是构建一个与领域无关、语言无关、经过统计训练的浅层语义解释器。这是一个复杂的、健壮的、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。这项研究解决了关键的科学问题，包括将表面形式和语义结构联系起来的可自动提取的语言特征的性质，特征和表示在语言中的健壮性程度，以及监督和非监督学习技术的组合。浅层语义解释器在英汉两种语言中都有工作，它将句子分析成包含谓词及其论元的命题，并用一组语义角色(施事、受事、工具、地点等)进行标记。该项目依赖于统计机器学习范式，包括从两个大型语义标签数据库(FrameNet和PropBank)提取特征的监督方法，以及无监督和轻度监督的方法，如图形模型聚类、联合训练和主动学习，包括各种复杂的语言特征。该项目的结果将允许将浅层语义表示纳入包括信息提取和机器翻译在内的所有关键自然语言处理应用程序中。这项研究还将提供有关暗示意义的语言特征的重要知识。最后，该项目将为其他研究提供关键的公开数据库。这些数据库包括FrameNet和PropBank的组合数据库，这将是使用语义网和一般本体的应用程序的丰富资源，以及具有丰富注释的中文PropBank，以及对机器翻译研究有用的对齐的英汉语料库。

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.