RI-Small: Efficient hidden structure annotation via structural multiple-sequence alignments

该项目的重点是开发自然语言的有限状态句法处理模型，该模型使用编码通过多序列比对(MSA)技术获得的全局结构约束的特征，以显著提高准确性，而不需要昂贵的上下文无关推理。MSA在计算生物学中被广泛用于建立有限状态模型，该模型捕捉序列(例如，RNA二级结构)中的长距离依赖关系。给定一大组MSA格式的功能比对序列，可以构建允许新序列与给定MSA进行有效比对的有限状态模型。在自然语言处理(NLP)中，很少使用MSA技术来表征语音或语义相似性。该项目正在探索同一语言中语义无关的字符串之间的纯句法功能对齐的定义，以定义用于构建有限状态句法模型的结构MSA。该项目有两个具体目标。第一个目标是开发自然语言序列处理算法和模型，所述自然语言序列处理算法和模型能够：a)相对于句法功能定义序列比对；b)基于所定义的功能比对来构建结构MSA；c)导出有限状态模型以有效地将新序列与所构建的MSA进行比对；以及d)从与MSA的比对中提取特征以用于改进的序列建模。第二个目标是在多个领域和语言的大量大规模文本处理应用程序中对该方法进行经验验证。由此产生的算法有望提供改进的有限状态自然语言模型，这将有助于关键文本处理应用中的最先进技术。

The focus of this project is to develop finite-state syntactic processing models for natural language that use features encoding global structural constraints derived through multiple sequence alignment (MSA) techniques, to significantly improve accuracy without expensive context-free inference. MSAs are widely used in computational biology for building finite-state models that capture long-distance dependencies in sequences (e.g., in RNA secondary structure). Given a large set of functionally aligned sequences in MSA format, finite-state models can be constructed that allow for the efficient alignment of new sequences with the given MSA. In natural language processing (NLP), only very rarely have MSA techniques been used, and then to characterize phonetic or semantic similarity. This project is exploring the definition of a purely syntactic functional alignment between semantically unrelated strings from the same language, to define a structural MSA for constructing finite-state syntactic models. The project has two specific aims. The first aim is to develop natural language sequence processing algorithms and models that can: a) define sequence alignments with respect to syntactic function; b) build structural MSAs based on defined functional alignments; c) derive finite-state models to efficiently align new sequences with the built MSA; and d) extract features from an alignment with the MSA for improved sequence modeling. The second aim is to empirically validate this approach within a number of large-scale text processing applications in multiple domains and languages. The resulting algorithms are expected to provide improved finite-state natural language models that will contribute to the state-of-the-art in critical text processing applications.