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<Paper uid="C02-2025">
  <Title>The LinGO Redwoods Treebank Motivation and Preliminary Applications</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> The LinGO Redwoods initiative is a seed activity in the design and development of a new type of treebank. While several medium- to large-scale treebanks exist for English (and for other major languages), pre-existing publicly available resources exhibit the following limitations: (i) annotation is mono-stratal, either encoding topological (phrase structure) or tectogrammatical (dependency) information, (ii) the depth of linguistic information recorded is comparatively shallow, (iii) the design and format of linguistic representation in the tree-bank hard-wires a small, predefined range of ways in which information can be extracted from the treebank, and (iv) representations in existing treebanks are static and over the (often year- or decade-long) evolution of a large-scale treebank tend to fall behind the development of the field. LinGO Redwoods aims at the development of a novel treebanking methodology, rich in nature and dynamic both in the ways linguistic data can be retrieved from the treebank in varying granularity and in the constant evolution and regular updating of the treebank itself.</Paragraph>
    <Paragraph position="1"> Since October 2001, the project is working to build the foundations for this new type of treebank, to develop a basic set of tools for treebank construction and maintenance, and to construct an initial set of 10,000 annotated trees to be distributed together with the tools under an open-source license.</Paragraph>
    <Paragraph position="2"> 1 Why Another (Type of) Treebank? For the past decade or more, symbolic, linguistically oriented methods and statistical or machine learning approaches to NLP have often been perceived as incompatible or even competing paradigms. While shallow and probabilistic processing techniques have produced useful results in many classes of applications, they have not met the full range of needs for NLP, particularly where precise interpretation is important, or where the variety of linguistic expression is large relative to the amount of training data available. On the other hand, deep approaches to NLP have only recently achieved broad enough grammatical coverage and sufficient processing efficiency to allow the use of precise linguistic grammars in certain types of real-world applications.</Paragraph>
    <Paragraph position="3"> In particular, applications of broad-coverage analytical grammars for parsing or generation require the use of sophisticated statistical techniques for resolving ambiguities; the transfer of Head-Driven Phrase Structure Grammar (HPSG) systems into industry, for example, has amplified the need for general parse ranking, disambiguation, and robust recovery techniques. We observe general consensus on the necessity for bridging activities, combining symbolic and stochastic approaches to NLP. But although we find promising research in stochastic parsing in a number of frameworks, there is a lack of appropriately rich and dynamic language corpora for HPSG.</Paragraph>
    <Paragraph position="4"> Likewise, stochastic parsing has so far been focussed on information-extraction-type applications and lacks any depth of semantic interpretation. The Redwoods initiative is designed to fill in this gap.</Paragraph>
    <Paragraph position="5"> In the next section, we present some of the motivation for the LinGO Redwoods project as a treebank development process. Although construction of the treebank is in its early stages, we present in Section 3 some preliminary results of using the treebank data already acquired on concrete applications. We show, for instance, that even simple statistical models of parse ranking trained on the Redwoods corpus built so far can disambiguate parses with close to 80% accuracy.</Paragraph>
  </Section>
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