Development, implementation and testing of a discourse model for newspaper texts

Liddy, Elizabeth D.; McVearry, Kenneth A.; Paik, Woojin; Yu, Edmund S.; McKenna, Mary C.

doi:10.3115/1075671.1075707

Cited by 16 publications

(15 citation statements)

References 4 publications

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“…Enumerative systems enumerate all the subjects of interest (which may be organized hierarchically) whereas faceted systems define the A more common approach is to develop models based on elements being communicated by various document types, also referred to by Paice [88] and by Liddy and colleagues [89] (both citing van Dijk), as the document's superstructure. We could also consider this approach to be genre-based.…”

Section: Facets and Faceted Browsingmentioning

confidence: 99%

Semantic Components: A Model for Enhancing Retrieval of Domain- Specific Information

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2000

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Semantic component searching allows a user to refine a topical search by indicating a preference for documents containing specific semantic components or by indicating terms that should appear in specific semantic components. 2We investigate four aspects of semantic components in this research. First, we describe lessons learned from using two methods for developing schemas in two domains. Second, we demonstrate use of semantic components to express domainspecific concepts and relationships by mapping a published taxonomy of questions asked by family practice physicians to the semantic component schemas for two document collections about medical care. Third, we report the results of a user study, showing that manual semantic component indexing is comparable to manual keyword indexing with respect to time and perceived difficulty and suggesting that semantic component indexing may be more accurate and consistent than manual keyword indexing. Fourth, we report the results of an interactive searching study, demonstrating the ability of semantic components to enhance search results compared to a baseline system without semantic components.In addition, we contribute a formal description of the semantic components model, a prototype implementation of semantic component indexing software, and a prototype implementation adding semantic components to an existing commercial search engine.Finally, we analyze metrics for evaluating instances of semantic component indexing and keyword indexing and illustrate use of a session-based metric for evaluating multiple-query search sessions.

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Section: Facets and Faceted Browsingmentioning

confidence: 99%

Semantic Components: A Model for Enhancing Retrieval of Domain- Specific Information

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2000

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“…Moreover, there is a distinctive discourse or style [21]. Further, the collection of historical newspapers is distinctive because we have the complete history of news events rather than processing them as they are streamed.…”

Section: Layered Content and Community Modelsmentioning

confidence: 99%

A Framework for Text Processing and Supporting Access to Collections of Digitized Historical Newspapers

Allen

Japzon

Achananuparp

et al. 2007

Lecture Notes in Computer Science

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Abstract. Large quantities of historical newspapers are being digitized and OCRd. We describe a framework for processing the OCRd text to identify articles and extract metadata for them. We describe the article schema and provide examples of features that facilitate automatic indexing of them. For this processing, we employ lexical semantics, structural models, and community content. Furthermore, we describe visualization and summarization techniques that can be used to present the extracted events. Keywords:We would like to encourage you to list your keywords in this section.

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“…Each filled template is a legal Cyc query. TextWise Corporation has been developing natural language information retrieval software primarily for news articles (Liddy, 1995). Teknowledge intends to use the TextWise KNOWledge base Information Tools (BGSfOW-IT) to supply many instances to Cyc of facts discovered from news stories.…”

Section: Crisis Management Integrationmentioning

confidence: 99%

Building and Acquiring Interactionist Ontologies

Cohen¹

2002

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The public reporting burden for this collection of information is estimated to average 1 hour per response, including th gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments re information, including suggestions for reducing the burden, to Department of Defense, Washington Headquarters Service: u-* * 1215 Jefferson Davis Highw/ay, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanu.ny any otner provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid 0MB control number.PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS. REPORT DATE (DD-MM-YYYY)2. REPORT TYPE Final TITLE AND SUBTITLEBuilding and Acquiring Interactionist Ontologies AUTHOR(S)Paul R. Cohen . WORK UNIT NUMBER PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)Computer Science Department University of Massachusetts Amherst, MA 01003 SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)Air DISTRIBUTION/AVAILABILITY STATEMENTDistribution Statement A. Approved for public release; distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTOur HPKB effort was intended to determine whether physical schemas could be a semantic core for ontologies. This report describes the basic theory of physical schema and our work on re-use of knowledge from ontologies. An addendum to the report is a published summary of all the research in the entire HPKB program written by Prof Cohen (first author) and many of the HPKB principals. Technical Final ReportOur HPKB effort was intended to determine whether physical schemas could be a semantic core for ontologies. This report describes the basic theory of physical schemas and our work on re-use of knowledge from ontologies. An addendum to the report is a published summary of all the research in the entire HPKB program written by Prof. Cohen (first author) and many of the HPKB principals. Physical Schemas and the Semantic CoreMany situations can be understood in terms of relatively few, physical relationships called image schemas or physical schemas (Lakoff 1984, Lakoff and Johnson 1980). Let's consider some examples:In/Out The first preposition learned by children of all cultures is "in." This is because "in" corresponds to one of the most basic conceptual structures in our minds, the idea of inclusion, containment, being part of a group or activity, being in a state, and so on. If an intelligent agent understood the word "in" as we do, then it would understand that the following sentences share a core "in" meaning:The base is in the Changjong Sector. The attack is in progress. The red forces are contained. The wing is deep in enemy airspace. You're in the army, now. The troops are in a confident state of mind.Similarly, a core "out" meaning is common to the following sentences:The base is outside the theater of operations. The attack is not our problem.In one of these sentences "outside" has a spatial interpretation; the other sentence evo...

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Development, implementation and testing of a discourse model for newspaper texts

Cited by 16 publications

References 4 publications

Semantic Components: A Model for Enhancing Retrieval of Domain- Specific Information

Semantic Components: A Model for Enhancing Retrieval of Domain- Specific Information

A Framework for Text Processing and Supporting Access to Collections of Digitized Historical Newspapers

Building and Acquiring Interactionist Ontologies

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