@misc{mckenzie2011scaffolding, abstract = {English language learners (ELLs) spend a majority of their instructional time in mainstream classrooms with mainstream teachers. Reading is an area with which many ELLs are challenged when placed within mainstream classrooms. Scaffolding has been identified as one of the best teaching practices for helping students read. ELL students in a local elementary school were struggling, and school personnel implemented scaffolding in an effort to address student needs. The purpose of this mixed methods study was to examine how personnel in one diversely populated school employed scaffolding to accommodate ELLs. Vygotsky's social constructivist theory informed the study. Research questions were designed to elicit the teachers' perceptions related to the use of scaffolding for ELLs and to examine the impact scaffolding had on ELLs reading performance. The perceptions of 14 out of 15 participating teachers were investigated via focus group interviews that were transcribed. Observation data were gathered to determine teachers' use of particular strategies. Hatch's method for coding and categorical analysis was used. Emerging themes included "background knowledge," "comprehension" and "evaluation." Participating teachers felt scaffolding strategies were crucial for building a solid foundation for ELL academic success. Pre and posttest scores in reading of 105 ELLs were analyzed using a paired samples t test. There were statistically significant gains in 13 of 15 performance indicators over the 3-month cycle of instruction. Implications for social change include strategies for classroom teachers and their administrators concerning scaffolding reading instruction with ELLs in order to help these students increase their reading performance levels. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]}, author = {McKenzie, Lolita D.}, interhash = {fe7d7642636e567b3cf5849b1f2a7e6a}, intrahash = {978f317bca6298ff7bd03edd736b8981}, journal = {ProQuest LLC}, month = {1}, title = {Scaffolding English Language Learners' Reading Performance}, uniqueid = {ED536391|eric}, year = 2011 } @inproceedings{ls_leimeister, address = {Helsinki, Finland (accepted for publication)}, author = {Peters, C. and Leimeister, J. M.}, booktitle = {Eighth International Conference on Design Science Research in Information Systems and Technology (DESRIST)}, interhash = {df69ab07e7f21529d53d332ac882148c}, intrahash = {e3adde20541398943d6bfaa216d96137}, note = {JML_397}, title = {Blueprint-driven Telemedicine Process Modeling – A domain-specific Modeling Language}, year = 2013 } @inproceedings{zesch2007analysis, abstract = {In this paper, we discuss two graphs in Wikipedia (i) the article graph, and (ii) the category graph. We perform a graph-theoretic analysis of the category graph, and show that it is a scale-free, small world graph like other well-known lexical semantic networks. We substantiate our findings by transferring semantic relatedness algorithms defined on WordNet to the Wikipedia category graph. To assess the usefulness of the category graph as an NLP resource, we analyze its coverage and the performance of the transferred semantic relatedness algorithms. }, address = {Rochester}, author = {Zesch, Torsten and Gurevych, Iryna}, booktitle = {Proceedings of the TextGraphs-2 Workshop (NAACL-HLT)}, interhash = {0401e62edb9bfa85dd498cb40301c0cb}, intrahash = {332ed720a72bf069275f93485432314b}, month = apr, pages = {1--8}, publisher = {Association for Computational Linguistics}, title = {Analysis of the Wikipedia Category Graph for NLP Applications}, url = {http://acl.ldc.upenn.edu/W/W07/W07-02.pdf#page=11}, year = 2007 } @techreport{prudhommeaux2008sparql, abstract = {RDF is a directed, labeled graph data format for representing information in the Web. This specification defines the syntax and semantics of the SPARQL query language for RDF. SPARQL can be used to express queries across diverse data sources, whether the data is stored natively as RDF or viewed as RDF via middleware. SPARQL contains capabilities for querying required and optional graph patterns along with their conjunctions and disjunctions. SPARQL also supports extensible value testing and constraining queries by source RDF graph. The results of SPARQL queries can be results sets or RDF graphs. }, author = {Prud'hommeaux, Eric and Seaborne, Andy}, institution = {W3C}, interhash = {dc198da0f907f0129249cab866bbe3d4}, intrahash = {f156278e58de586730e51d791f3b5f69}, month = jan, title = {SPARQL Query Language for RDF}, type = {W3C Recommendation}, url = {http://www.w3.org/TR/rdf-sparql-query/}, year = 2008 } @inproceedings{Rudolph:2010:CMM:1858681.1858774, abstract = {We propose CMSMs, a novel type of generic compositional models for syntactic and semantic aspects of natural language, based on matrix multiplication. We argue for the structural and cognitive plausibility of this model and show that it is able to cover and combine various common compositional NLP approaches ranging from statistical word space models to symbolic grammar formalisms.}, acmid = {1858774}, address = {Stroudsburg, PA, USA}, author = {Rudolph, Sebastian and Giesbrecht, Eugenie}, booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics}, interhash = {6594500d38a361829aeb3ef7889a1709}, intrahash = {05ec57c39e9b945deb674c3b616eac8f}, location = {Uppsala, Sweden}, numpages = {10}, pages = {907--916}, publisher = {Association for Computational Linguistics}, series = {ACL '10}, title = {Compositional matrix-space models of language}, url = {http://dl.acm.org/citation.cfm?id=1858681.1858774}, year = 2010 } @article{resnik2003parallel, abstract = {Parallel corpora have become an essential resource for work in multilingual natural language processing. In this article, we report on our work using the STRAND system for mining parallel text on the World Wide Web,first reviewing the original algorithm and results and then presenting a set of significant enhancements. These enhancements include the use of supervised learning based on structural features of documents to improve classification performance, a new content-based measure of translational equivalence, and adaptation of the system to take advantage of the Internet Archive for mining parallel text from the Web on a large scale. Finally, the value of these techniques is demonstrated in the construction of a significant parallel corpus for a low-density language pair.}, acmid = {964753}, address = {Cambridge, MA, USA}, author = {Resnik, Philip and Smith, Noah A.}, doi = {10.1162/089120103322711578}, interhash = {b23f5b4586fb7dd07c28c376b08c0eda}, intrahash = {2fdc5044a0d669f6766edaaceaae2bc3}, issn = {0891-2017}, issue_date = {September 2003}, journal = {Computational Linguistics}, month = sep, number = 3, numpages = {32}, pages = {349--380}, publisher = {MIT Press}, title = {The Web as a parallel corpus}, url = {http://dx.doi.org/10.1162/089120103322711578}, volume = 29, year = 2003 } @inproceedings{cavnar1994ngrambased, 1 = {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}, abstract = {Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors in email, and character recognition errors in documents that come through OCR. Text categorization must work reliably on all input, and thus must tolerate some level of these kinds of problems. We describe here an N-gram-based approach to text categorization that is tolerant of textual errors. The system is small, fast and robust. This system worked very well for language classification, achieving in one test a 99.8 % correct classification rate on Usenet newsgroup articles written in different languages. The system also worked reasonably well for classifying articles from a number of different computer-oriented newsgroups according to subject, achieving as high as an 80 % correct classification rate. There are also several obvious directions for improving the system's classification performance in those cases where it did not do as well. The system is based on calculating and comparing profiles of N-gram frequencies. First, we use the system to compute profiles on training set data that represent the various categories, e.g., language samples or newsgroup content samples. Then the system computes a profile for a particular document that is to be classified. Finally, the system computes a distance measure between the document's profile and each of the}, added = {2009-08-17 11:40:57 +0200}, address = {Las Vegas}, author = {Cavnar, William B. and Trenkle, John M.}, booktitle = {Symposium On Document Analysis and Information Retrieval}, interhash = {f0473fcb06a7b07f51bbfdc71b4b063c}, intrahash = {6922ef3ab653ff35cbe9117227816a24}, modified = {2010-01-04 09:30:08 +0100}, pages = {161--175}, title = {N-Gram-Based Text Categorization}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.9367}, urldate = {20.10.2008}, year = 1994 } @book{jurafsky2000speech, asin = {0130950696}, author = {Jurafsky, Daniel and Martin, James H.}, dewey = {410.285}, ean = {9780130950697}, edition = 1, interhash = {ae1205b1f526d068fc9364510bf99418}, intrahash = {25110e6691b5ee9dbe97216ce087487f}, isbn = {0130950696}, note = {neue Auflage kommt im Frühjahr 2008}, publisher = {Prentice Hall}, title = {Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (Prentice Hall Series in Artificial Intelligence)}, url = {http://www.amazon.com/gp/redirect.html%3FASIN=0130950696%26tag=ws%26lcode=xm2%26cID=2025%26ccmID=165953%26location=/o/ASIN/0130950696%253FSubscriptionId=13CT5CVB80YFWJEPWS02}, year = 2000 } @book{manning1999fsn, author = {Manning, C.D. and Sch{\"u}tze, H.}, interhash = {a81df02f92f266a51183fe936f588a08}, intrahash = {fbcf85d3ed7280552e3d8a5dbe76a525}, publisher = {MIT Press}, title = {{Foundations of statistical natural language processing}}, year = 1999 } @article{ponte1998lma, author = {Ponte, J.M. and Croft, W.B.}, booktitle = {Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval}, interhash = {7d5d602886fa34e485cf6194f70bd793}, intrahash = {229b65aa2b99b2f27bc990840e79b3eb}, organization = {ACM New York, NY, USA}, pages = {275--281}, title = {{A language modeling approach to information retrieval}}, year = 1998 } @article{harabagiu1998knowledge, author = {Harabagiu, S.M. and Moldovan, D.I.}, interhash = {cde2156fc8b89dde45b0de609ef8c912}, intrahash = {d9504ca1a2e78bde2c9f1d14ad8eab00}, journal = {WordNet-An Electronic Lexical Database}, pages = {379--405}, title = {{Knowledge processing on an extended wordnet}}, url = {http://scholar.google.de/scholar.bib?q=info:Wx8vDCCwS-YJ:scholar.google.com/&output=citation&hl=de&ct=citation&cd=1}, year = 1998 } @inproceedings{veres2006language, abstract = {Folksonomies are classification schemes that emerge from the collective actions of users who tag resources with an unrestricted set of key terms. There has been a flurry of activity in this domain recently with a number of high profile web sites andsearch engines adopting the practice. They have sparked a great deal of excitement and debate in the popular and technicalliterature, accompanied by a number of analyses of the statistical properties of tagging behavior. However, none has addressedthe deep nature of folksonomies. What is the nature of a tag? Where does it come from? How is it related to a resource? Inthis paper we present a study in which the linguistic properties of folksonomies reveal them to contain, on the one hand,tags that are similar to standard categories in taxonomies. But on the other hand, they contain additional tags to describeclass properties. The implications of the findings for the relationship between folksonomy and ontology are discussed.}, address = {Berlin/Heidelberg}, author = {Veres, Csaba}, booktitle = {Natural Language Processing and Information Systems}, doi = {10.1007/11765448}, editor = {Kop, Christian and Fliedl, Günther and Mayr, Heinrich C. and Métais, Elisabeth}, interhash = {1787dec43f3c11153fc9d2617af8829c}, intrahash = {d0e5be1774a6094049df3e6d604f1957}, isbn = {978-3-540-34616-6}, issn = {0302-9743}, pages = {58--69}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {The Language of Folksonomies: What Tags Reveal About User Classification}, url = {http://dx.doi.org/10.1007/11765448_6}, volume = 3999, year = 2006 } @book{0130950696, asin = {0130950696}, author = {Jurafsky, Daniel and Martin, James H.}, dewey = {410.285}, ean = {9780130950697}, edition = 1, interhash = {ae1205b1f526d068fc9364510bf99418}, intrahash = {25110e6691b5ee9dbe97216ce087487f}, isbn = {0130950696}, publisher = {Prentice Hall}, title = {Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (Prentice Hall Series in Artificial Intelligence)}, url = {http://www.amazon.com/Speech-Language-Processing-Introduction-Computational/dp/0130950696%3FSubscriptionId%3D13CT5CVB80YFWJEPWS02%26tag%3Dws%26linkCode%3Dxm2%26camp%3D2025%26creative%3D165953%26creativeASIN%3D0130950696}, year = 2000 } @article{schoenefeld1988say, author = {Schoenfeld, Robert}, doi = {10.1002/anie.198810501}, interhash = {8e06af7a5a9d639834c44363af975c63}, intrahash = {9d15e1d0bab6dde0c552c8e7c82ff0fb}, journal = {Angewandte Chemie International Edition in English}, number = 8, pages = {1050-1057}, source = {Wiley Plain Text file from www3.interscience.wiley.com}, title = {Say It in English, Please!}, typesource = {Wiley}, url = {http://www3.interscience.wiley.com/cgi-bin/abstract/106598330/ABSTRACT?CRETRY=1&SRETRY=0}, volume = 27, year = 1988 }