SGER: Sense in Context: Construction of a Selectional Context Dictionary for English Clauses

这项研究探索了一种构建自然语言语境词典的新框架，即为该语言中的论元采取预测词建立一个选择语境的计算词汇数据库。这个项目的目标是从非常大的语料库中半自动地识别单词使用的规范，然后使用受限的语义类型和句法识别符系统将这些规范编码为选择上下文。将使用一种名为语料库模式分析(CPA)的新方法，它与LKB构建中的现有方法有很大不同。这项工作不是构建MRD种子词典、手工制作的词典(WordNet、EuroWordNet)和语料库信息词典(FrameNet)，而是在特定的语言建模原则的约束下，采用一种方法来构建“语料库驱动”的词典。这样的“选择上下文词典”通过捕获语言中谓词的更丰富级别的选择上下文，克服了以前LKB方法的许多缺点。这可以通过使用对谓词参数的浅层解析和来自浅层语义类型系统的类型赋值来实现。这项技术将根据既定的方法对结果的选择性区分能力进行评估；即针对未见文本的带注释测试集的准确率和召回率测量。

ABSTRACTThis research explores a novel framework for constructing a context dictionary for natural language; that is, a computational lexical database of selectional contexts for argument-taking predicators in the language. The goal of this project is to semi-automatically identify, from very large corpora of the language, the norms of word usage, and then to encode these norms as selectional contexts using a restricted system of semantic types and syntactic identifiers.A novel methodology, called Corpus Pattern Analysis (CPA), which is quite different from established methods in LKB construction, will be used. Rather than building MRD-seeded lexicons, hand-crafted lexicons (WordNet, EuroWordNet), and corpus-informed lexicons (FrameNet), this work will employ a methodology for building a "corpus-driven" lexicon, constrained by specific linguistic modeling principles. Such a "selectional context dictionary" overcomes many of the shortcomings of previous LKB approaches by capturing a richer level of selectional context for predicates in the language. This is possible with the use of shallow parsing of predicate arguments, and the assignment of types from a shallow semantic type system. The technique will be evaluated the selectional discriminatory capabilities of the results, according to established methods; namely, precision and recall measurements against an annotated test set of unseen text.