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- 105 sameAs 105.
- 105 abstract "We introduce the corpus of United States Congressional bills from 1947 to 1998 for use by language research communities. The U.S. Policy Agenda Legislation Corpus Volume 1 (USPALCV1) includes more than 375,000 legislative bills annotated with a hierarchical policy area category. The human annotations in USPALCV1 have been reliably applied over time to enable social science analysis of legislative trends. The corpus is a member of an emerging family of corpora that are annotated by policy area to enable comparative parallel trend recognition across countries and domains (legislation, political speeches, newswire articles, budgetary expenditures, web sites, etc.). This paper describes the origins of the corpus, its creation, ways to access it, design criteria, and an analysis with common supervised machine learning methods. The use of machine learning methods establishes a baseline proposed modeling for the topic classification of legal documents.".
- 105 hasAuthorList authorList.
- 105 hasTopic Linguistics.
- 105 isPartOf proceedings.
- 105 keyword "Corpus (creation, annotation, etc.)".
- 105 keyword "Document Classification, Text categorisation".
- 105 keyword "Text mining".
- 105 title "The U.S. Policy Agenda Legislation Corpus Volume 1 - a Language Resource from 1947 - 1998".
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- item-1 hasContent stephen-purpura.
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- item-2 hasContent john-wilkerson.
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- item-3 hasContent dustin-hillard.
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- 106 creator ben-wellner.
- 106 creator dave-harris.
- 106 creator inderjeet-mani.
- 106 creator janet-hitzeman.
- 106 creator justin-richer.
- 106 creator rob-quimby.
- 106 type InProceedings.
- 106 label "SpatialML: Annotation Scheme, Corpora, and Tools".
- 106 sameAs 106.
- 106 abstract "SpatialML is an annotation scheme for marking up references to places in natural language. It covers both named and nominal references to places, grounding them where possible with geo-coordinates, including both relative and absolute locations, and characterizes relationships among places in terms of a region calculus. A freely available annotation editor has been developed for SpatialML, along with a corpus of annotated documents released by the Linguistic Data Consortium. Inter-annotator agreement on SpatialML is 77.0 F-measure for extents on that corpus. An automatic tagger for SpatialML extents scores 78.5 F-measure. A disambiguator scores 93.0 F-measure and 93.4 Predictive Accuracy. In adapting the extent tagger to new domains, merging the training data from the above corpus with annotated data in the new domain provides the best performance.".
- 106 hasAuthorList authorList.
- 106 hasTopic Linguistics.
- 106 isPartOf proceedings.
- 106 keyword "Corpus (creation, annotation, etc.)".
- 106 keyword "Information Extraction, Information Retrieval".
- 106 keyword "Semantics".
- 106 title "SpatialML: Annotation Scheme, Corpora, and Tools".
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- item-1 hasContent inderjeet-mani.
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- item-2 hasContent janet-hitzeman.
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- item-3 hasContent justin-richer.
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- item-6 hasContent ben-wellner.
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- 107 creator hitoshi-isahara.
- 107 creator koichi-nakao.
- 107 creator masaki-murata.
- 107 creator qing-ma.
- 107 type InProceedings.
- 107 label "Selection of Japanese-English Equivalents by Integrating High-quality Corpora and Huge Amounts of Web Data".
- 107 sameAs 107.
- 107 abstract "As a first step to developing systems that enable non-native speakers to output near-perfect English sentences for given mixed English-Japanese sentences, we propose new approaches for selecting English equivalents by using the number of hits for various contexts in large English corpora. As the large English corpora, we not only used the huge amounts of Web data but also the manually compiled large, high-quality English corpora. Using high-quality corpora enables us to accurately select equivalents, and using huge amounts of Web data enables us to resolve the problem of the shortage of hits that normally occurs when using only high-quality corpora. The types and lengths of contexts used to select equivalents are variable and optimally determined according to the number of hits in the corpora, so that performance can be further refined. Computer experiments showed that the precision of our methods was much higher than that of the existing methods for equivalent selection.".
- 107 hasAuthorList authorList.
- 107 hasTopic Linguistics.
- 107 isPartOf proceedings.
- 107 keyword "Corpus (creation, annotation, etc.)".
- 107 keyword "Machine Translation, SpeechToSpeech Translation".
- 107 keyword "Tools, systems, applications".
- 107 title "Selection of Japanese-English Equivalents by Integrating High-quality Corpora and Huge Amounts of Web Data".
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- item-1 hasContent qing-ma.
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- item-2 hasContent koichi-nakao.