Efficient Indexing and Querying over Syntactically Annotated Trees

TL;DR

This paper introduces a novel indexing scheme and root-split coding for efficiently querying large collections of syntactically parsed trees, significantly reducing index size and query response times.

Contribution

It presents a new indexing method and a root-split coding scheme that enhance performance and scalability of querying over syntactically annotated trees.

Findings

Root-split coding reduces index size by 50-80%.

The proposed index outperforms previous methods by at least an order of magnitude in query response time.

The approach enables efficient searching over large syntactic tree corpora.

Abstract

Natural language text corpora are often available as sets of syntactically parsed trees. A wide range of expressive tree queries are possible over such parsed trees that open a new avenue in searching over natural language text. They not only allow for querying roles and relationships within sentences, but also improve search effectiveness compared to flat keyword queries. One major drawback of current systems supporting querying over parsed text is the performance of evaluating queries over large data. In this paper we propose a novel indexing scheme over unique subtrees as index keys. We also propose a novel root-split coding scheme that stores subtree structural information only partially, thus reducing index size and improving querying performance. Our extensive set of experiments show that root-split coding reduces the index size of any interval coding which stores individual node numbers by a factor of 50% to 80%, depending on the sizes of subtrees indexed. Moreover, We show that our index using root-split coding, outperforms previous approaches by at least an order of magnitude in terms of the response time of queries.