Representation trees and string-tree correspondences

Boitet, Christian; Yusoff, Zaharin

doi:10.3115/991635.991648

Cited by 18 publications

(18 citation statements)

References 3 publications

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“…With respect to this issue, in our proposed solution, we shall look at the aspect that can maximize the document content integrity but still able to separate different annotation layers. Similar to [2], a flexible annotation schema for linguistic contents has been introduced by [3]. In this work, contents like a sentence can be linked to any type of linguistic structure (e.g.…”

Section: Addressing Multiple Annotationsmentioning

confidence: 99%

Representing Annotations in XML Document using String-Trees Model

Gan

2014

Proceedings of the 16th International Conference on Information Integration and Web-Based Applications &Amp; Services

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The flexibility of XML allows document to be annotated easily. However, these annotations come from different sources like Wordnet thesaurus, POS, DTD, semantic roles etc. These annotations can either be combined in the same document or captured separately in different document. The former, though richer in annotations, may look messy and requires more parsing time. The latter needs control of document consistency. This paper proposes a string-trees model to represent XML document for multiple sources of annotations. This model extends the existing string-tree structure for linguistic content in order to support structured contents of XML document. In this paper, we describe how this model is refined and applied on XML document.

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Section: Addressing Multiple Annotationsmentioning

confidence: 99%

Representing Annotations in XML Document using String-Trees Model

Gan

2014

Proceedings of the 16th International Conference on Information Integration and Web-Based Applications &Amp; Services

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“…We call this tree the Query Target tree. Here, a string tree correspondence method [1] will be used to record the correspondence between structured query string and its query representation, i.e. Query Target tree.…”

Section: B Multiple Structures Solutionmentioning

confidence: 99%

Automated query transformation for searching semantically rich structured collections

Gan

Phang

2011

2011 International Conference on Semantic Technology and Information Retrieval

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The availability of semantically rich structured resources makes the search of such resources on the web even more interesting, where the formation of a meaningful structured query is now possible for better retrieval. Incorporation of concepts like role, category, topic, class, attributes, etc. to indicate the search target and constraints in a structured query enable better definition of information needs. However, the varieties of usages in application contexts create heterogeneity of structures definition and presentation, thus making it hard of using this information when formulating structured query for a search process. Hence, we are motivated to automate transformation of unstructured queries (which are more common for web user), to structured queries (which support better specification of information needs). This paper first explores two important issues arisen in the process of query transformation, i.e. multiple structures (with respect to the needs of handling multiple structured queries type) and multiple semantics (with respect to the needs of handling different collections of structured resources). Along with these issues, we define research problems, followed by an approach of mediating multiple structures and multiple semantics to support the query transformation from unstructured to structured form.

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“…In practice, a node N,n of a SN is made up of two data: * SNODE: a sequence which represents a substring of a sentence. For example, the words of the sentence "On avait denombre cent vingt-neuf candidats" have to SNODE: On [1,2], avait [3][4][5][6][7], denombre [8][9][10][11][12][13][14][15], etc. This data is based on the proposal of [5] Structured String-Tree Correspondences (SSTC).…”

Section: Correspondence Ofthe Dependency Structuresmentioning

confidence: 99%

“…They correspond to the nodes on[J-2], avait [3][4][5][6][7], dcnombre [8][9][10][11][12][13][14][15] of SNbase. The fourth node cent vingt-neuf [16][17][18][19] of SN2 verifies the second rule, so it is inserted into SNbase.…”

Section: Correspondence Ofthe Dependency Structuresmentioning

confidence: 99%

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Improving Dependency Analysis by Syntactic Parser Combination

Brunet-Manquat

2005 International Conference on Natural Language Processing and Knowledge Engineering

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The goal of this article is to present our work about a combination of several syntactic parsers to produce a more robust parser. We have built a platform which allows us to compare syntactic parsers for a given language by splitting their results in elementary pieces, normalizing them, and comparing them with reference results. The same platform is used to combine several parsers to produce a dependency parser that has larger coverage and is more robust than its component parsers. In the future, it should be possible to "compile" the knowledge extracted from several analyzers into an autonomous dependency parser.

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Representation trees and string-tree correspondences

Cited by 18 publications

References 3 publications

Representing Annotations in XML Document using String-Trees Model

Representing Annotations in XML Document using String-Trees Model

Automated query transformation for searching semantically rich structured collections

Improving Dependency Analysis by Syntactic Parser Combination

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