ITR: Domain-Independent Semantic Interpretation

这个项目的目标是建立一个独立于领域、独立于语言、经过统计训练的浅层语义解释器。这是一个复杂、健壮、广泛使用的工具，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可提取语言特征的性质，跨语言的特征和表示的鲁棒程度，以及监督和无监督学习技术的结合。浅层语义解释器通过将句子分析成包含谓词及其论证的命题，并通过一系列语义角色（Agent, Patient, Instrument， Location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例，包括监督方法，其中从两个大型语义标记数据库（FrameNet和PropBank）中提取特征，以及无监督和轻度监督方法，如图形模型聚类，共同训练和主动学习，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整合到一系列关键的自然语言处理应用程序中，包括信息提取和机器翻译。这项研究还将提供关于作为意义线索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共数据库。其中包括框架网和PropBank数据库的组合，它将成为使用语义网和本体的应用程序的丰富资源，以及一个丰富注释的中文PropBank，以及对机器翻译研究有用的中英文语料库。

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.