使用基于对齐的学习的自举递归和递归
Bootstrapping Syntax and Recursion using Alignment-Based Learning
摘要：
本文介绍了一种新的无监督学习算法，基于对齐的句子和哈里斯（1951）的概念的可扩展性。该算法被应用到一个未标记的，非结构化的语料库的自然语言句子，从而在一个标记，括号版本的语料库。首先，该算法将语料库中的所有句子成对对齐，从而产生由两个句子中相似的句子部分和不相似的部分组成的句子分区。该信息用于查找（可能重叠的）成分。接下来，算法选择（非重叠的）成分。该算法的几个实例被应用于ATIS语料库（Marcus等人，Openbaar Vervoer Informatie Systemem（OVIS）是Public Transport信息系统的缩写。语料库（Bonnema等人，1997年）。除了有希望的数值结果，最引人注目的结果是，即使是最简单的算法基于对齐学习递归。
英文摘要：
This paper introduces a new type of unsupervised learning algorithm, based on the alignment of sentences and Harris's (1951) notion of interchangeability. The algorithm is applied to an untagged, unstructured corpus of natural language sentences, resulting in a labelled, bracketed version of the corpus. Firstly, the algorithm aligns all sentences in the corpus in pairs, resulting in a partition of the sentences consisting of parts of the sentences that are similar in both sentences and parts that are dissimilar. This information is used to find (possibly overlapping) constituents. Next, the algorithm selects (non-overlapping) constituents. Several instances of the algorithm are applied to the ATIS corpus (Marcus et al., 1993) and the OVIS (Openbaar Vervoer Informatie Systeem (OVIS) stands for Public Transport Information System.) corpus (Bonnema et al., 1997). Apart from the promising numerical results, the most striking result is that even the simplest algorithm based on alignment learns recursion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
