1,076 research outputs found

    Unity in diversity : integrating differing linguistic data in TUSNELDA

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    This paper describes the creation and preparation of TUSNELDA, a collection of corpus data built for linguistic research. This collection contains a number of linguistically annotated corpora which differ in various aspects such as language, text sorts / data types, encoded annotation levels, and linguistic theories underlying the annotation. The paper focuses on this variation on the one hand and the way how these heterogeneous data are integrated into one resource on the other hand

    Treebank querying with GrETEL 3 : bigger, faster, stronger

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    We describe the new version of GrETEL (http://gretel.ccl.kuleuven.be/gretel3), an online tool which allows users to query treebanks by means of a natural language example (example-based search) or via a formal query (XPath search). The new release comprises an update to the interface and considerable improvements in the back-end search mechanism. The update of the front-end is based on user suggestions. In addition to an overall design update, major changes include a more intuitive query builder in the example-based search mode and a visualizer for syntax trees that is compatible with all modern browsers. Moreover, the results are presented to the user as soon as they are found, so users can browse the matching sentences before the treebank search is completed. We will demonstrate that those changes considerably improve the query procedure. The update of the back-end mainly includes optimizing the search algorithm for querying the (very) large SoNaR treebank. Querying this 500-million word treebank was already made possible in the previous version of GrETEL, but due to the complex search mechanism this often resulted in long query times or even a timeout before the search completed. The improved version of the search algorithm results in faster query times and more accurate search results, which greatly enhances the usability of the SoNaR treebank for linguistic research

    A Modular and Flexible Architecture for an Integrated Corpus Query System

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    The paper describes the architecture of an integrated and extensible corpus query system developed at the University of Stuttgart and gives examples of some of the modules realized within this architecture. The modules form the core of a corpus workbench. Within the proposed architecture, information required for the evaluation of queries may be derived from different knowledge sources (the corpus text, databases, on-line thesauri) and by different means: either through direct lookup in a database or by calling external tools which may infer the necessary information at the time of query evaluation. The information available and the method of information access can be stated declaratively and individually for each corpus, leading to a flexible, extensible and modular corpus workbench.Comment: 10 pages, uuencoded gzip'ped PostScript; presented at COMPLEX'9

    Treebanks gone bad: generating a treebank of ungrammatical English

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    This paper describes how a treebank of ungrammatical sentences can be created from a treebank of well-formed sentences. The treebank creation procedure involves the automatic introduction of frequently occurring grammatical errors into the sentences in an existing treebank, and the minimal transformation of the analyses in the treebank so that they describe the newly created ill-formed sentences. Such a treebank can be used to test how well a parser is able to ignore grammatical errors in texts (as people can), and can be used to induce a grammar capable of analysing such sentences. This paper also demonstrates the first of these uses

    Querying large treebanks : benchmarking GrETEL indexing

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    The amount of data that is available for research grows rapidly, yet technology to efficiently interpret and excavate these data lags behind. For instance, when using large treebanks for linguistic research, the speed of a query leaves much to be desired. GrETEL Indexing, or GrInding, tackles this issue. The idea behind GrInding is to make the search space as small as possible before actually starting the treebank search, by pre-processing the treebank at hand. We recursively divide the treebank into smaller parts, called subtree-banks, which are then converted into database files. All subtree-banks are organized according to their linguistic dependency pattern, and labeled as such. Additionally, general patterns are linked to more specific ones. By doing so, we create millions of databases, and given a linguistic structure we know in which databases that structure can occur, leading up to a significant efficiency boost. We present the results of a benchmark experiment, testing the effect of the GrInding procedure on the SoNaR-500 treebank

    An Integrated Framework for Treebanks and Multilayer Annotations

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    Treebank formats and associated software tools are proliferating rapidly, with little consideration for interoperability. We survey a wide variety of treebank structures and operations, and show how they can be mapped onto the annotation graph model, and leading to an integrated framework encompassing tree and non-tree annotations alike. This development opens up new possibilities for managing and exploiting multilayer annotations.Comment: 8 page

    Detecting grammatical errors in machine translation output using dependency parsing and treebank querying

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    Despite the recent advances in the field of machine translation (MT), MT systems cannot guarantee that the sentences they produce will be fluent and coherent in both syntax and semantics. Detecting and highlighting errors in machine-translated sentences can help post-editors to focus on the erroneous fragments that need to be corrected. This paper presents two methods for detecting grammatical errors in Dutch machine-translated text, using dependency parsing and treebank querying. We test our approach on the output of a statistical and a rule-based MT system for English-Dutch and evaluate the performance on sentence and word-level. The results show that our method can be used to detect grammatical errors with high accuracy on sentence-level in both types of MT output
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