A variety of query systems have been developed for interrogating parsed corpora , or treebanks . With the arrival of efficient , widecoverage parsers , it is feasible to create very large databases of trees . However , existing approaches that use in-memory search , or relational or XML database technologies , do not scale up . We describe a method for storage , indexing , and query of treebanks that uses an information retrieval engine . Several experiments with a large treebank demonstrate excellent scaling characteristics for a wide range of query types . This work facilitates the curation of much larger treebanks , and enables them to be used effectively in a variety of scientific and engineering tasks . The problem of representing and querying linguistic annotations has been an active area of research for several years . Much of the work has grown from efforts to curate large databases of annotated text such as treebanks , for use in developing and testing language technologies ( Marcus et al . , 1993 ; Abeillé , 2003 ; Hockenmaier and Steedman , 2007 ) . At least a dozen linguistic tree query languages have been developed for interrogating treebanks ( see § 2 ) . While high quality syntactic parsers are able to efficiently annotate large quantities of English text ( Clark and Curran , 2007 ) , existing approaches to query do not work on the same scale . Many existing systems load the entire corpus into memory and check a user-supplied query against every tree . Others avoid the memory limitation , and use relational or XML database systems . Although these have built-in support for indexes , they do not scale up either ( Ghodke and Bird , 2008 ; Zhang et al . , 2001 ) ) . The ability to interrogate large collections of parsed text has important practical applications . First , it opens the way to a new kind of information retrieval ( IR ) that is sensitive to syntactic information , permitting users to do more focussed search . At the simplest level , an ambiguous query term like wind or park could be disambiguated with the help of a POS tag ( e.g. wind / N , park / V ) . ( Existing IR engines already support query with part-of-speech tags ( Chowdhury and McCabe , 1998 ) ) . More complex queries could stipulate the syntactic category of apple is in subject position . A second benefit of large scale tree query is for natural language processing . For example , we might compute the likelihood that a given noun appears as the agent or patient of a verb , as a measure of animacy . We can use features derived from syntactic trees in order to support semantic role labeling , language modeling , and information extraction ( Chen and Rambow , 2003 ; Collins et al . , 2005 ; Hakenberg et al . , 2009 ) . A further benefit for natural language processing , though not yet realized , is for a treebank and query engine to provide the underlying storage and retrieval for a variety of linguistic applications . Just as a relational database is present in most business applications , providing reliable and efficient access to relational data , such a system would act as a repository of annotated texts , and expose an expressive API to client applications . A third benefit of large scale tree query is to support syntactic investigations , e.g. for develop-ing syntactic theories or preparing materials for language learners . Published treebanks will usually not attest particular words in the context of some infrequent construction , to the detriment of syntactic studies that make predictions about such combinations , and language learners wanting to see instances of some construction involving words from some specialized topic . A much larger treebank alleviates these problems . To improve recall performance , multiple parses for a given sentence could be stored ( possibly derived from different parsers ) . A fourth benefit for large scale tree query is to support the curation of treebanks , a major enterprise in its own right ( Abeillé , 2003 ) . Manual selection and correction of automatically generated parse trees is a substantial part of the task of preparing a treebank . At the point of making such decisions , it is often helpful for an annotator to view existing annotations of a given construction which have already been manually validated ( Hiroshi et al . , 2005 ) . Occasionally , an earlier annotation decision may need to be reconsidered in the light of new examples , leading to further queries and to corrections that are spread across the whole corpus ( Wallis , 2003 ; Xue et al . , 2005 ) . This paper explores a new methods for scaling up tree query using an IR engine . In § 2 we describe existing tree query systems , elaborating on the design decisions , and on key aspects of their implementation and performance . In § 3 we describe a method for indexing trees using an IR engine , and discuss the details of our open source implementation . In § 4 we report results from a variety of experiments involving two data collections . The first collection contains of 5.5 million parsed trees , two orders of magnitude larger than those used by existing tree query systems , while the second collection contains 26.5 million trees . We have shown how an IR engine can be used to build a high performance tree query system . It outperforms existing approaches using indexless inmemory search , or custom indexes , or relational database systems , or XML database systems . We reported the results of a variety of experiments to demonstrate the efficiency of query for a variety of query types on two treebanks consisting of around 5 and 26 million sentences , more than two orders of magnitude larger than what existing systems support . The approach is quite general , and not limited to particular treebank formats or query languages . This work suggests that web-scale tree query may soon be feasible . This opens the door to some interesting possibilities : augmenting web search with syntactic constraints , the ability discover rare examples of particular syntactic constructions , and as a technique for garnering better statistics and more sensitive features for the purpose of constructing language models .

Challenge: Developments of treebanks achieve creating large databases of trees however existing search or database technologies for them do not scale in size.
Approach: They describe a method for storage, indexing, and query of treebank based on an information retrieve engine to scale up tree query.
Outcome: The proposed method outperforms existing systems on a wide range of query types and can hold two large treebanks that existing systems cannot support.