Linguistic Linked Data for Lexicography
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"Preserve Analysis : Saddle Mountain"
PRESERVE ANALYSIS: SADDLE MOUNTAIN Pre pare d by PAUL B. ALABACK ROB ERT E. FRENKE L OREGON NATURAL AREA PRESERVES ADVISORY COMMITTEE to the STATE LAND BOARD Salem. Oregon October, 1978 NATURAL AREA PRESERVES ADVISORY COMMITTEE to the STATE LAND BOARD Robert Straub Nonna Paul us Governor Clay Myers Secretary of State State Treasurer Members Robert Frenkel (Chairman), Corvallis Bill Burley (Vice Chainnan), Siletz Charles Collins, Roseburg Bruce Nolf, Bend Patricia Harris, Eugene Jean L. Siddall, Lake Oswego Ex-Officio Members Bob Maben Wi 11 i am S. Phe 1ps Department of Fish and Wildlife State Forestry Department Peter Bond J. Morris Johnson State Parks and Recreation Branch State System of Higher Education PRESERVE ANALYSIS: SADDLE MOUNTAIN prepared by Paul B. Alaback and Robert E. Frenkel Oregon Natural Area Preserves Advisory Committee to the State Land Board Salem, Oregon October, 1978 ----------- ------- iii PREFACE The purpose of this preserve analysis is to assemble and document the significant natural values of Saddle Mountain State Park to aid in deciding whether to recommend the dedication of a portion of Saddle r10untain State Park as a natural area preserve within the Oregon System of I~atural Areas. Preserve management, agency agreements, and manage ment planning are therefore not a function of this document. Because of the outstanding assemblage of wildflowers, many of which are rare, Saddle r·1ountain has long been a mecca for· botanists. It was from Oregon's botanists that the Committee initially received its first documentation of the natural area values of Saddle Mountain. Several Committee members and others contributed to the report through survey and documentation. -
Spell Checker
International Journal of Scientific and Research Publications, Volume 5, Issue 4, April 2015 1 ISSN 2250-3153 SPELL CHECKER Vibhakti V. Bhaire, Ashiki A. Jadhav, Pradnya A. Pashte, Mr. Magdum P.G ComputerEngineering, Rajendra Mane College of Engineering and Technology Abstract- Spell Checker project adds spell checking and For handling morphology language-dependent algorithm is an correction functionality to the windows based application by additional step. The spell-checker will need to consider different using autosuggestion technique. It helps the user to reduce typing forms of the same word, such as verbal forms, contractions, work, by identifying any spelling errors and making it easier to possessives, and plurals, even for a lightly inflected language like repeat searches .The main goal of the spell checker is to provide English. For many other languages, such as those featuring unified treatment of various spell correction. Firstly the spell agglutination and more complex declension and conjugation, this checking and correcting problem will be formally describe in part of the process is more complicated. order to provide a better understanding of these task. Spell checker and corrector is either stand-alone application capable of A spell checker carries out the following processes: processing a string of words or a text or as an embedded tool which is part of a larger application such as a word processor. • It scans the text and selects the words contained in it. Various search and replace algorithms are adopted to fit into the • domain of spell checker. Spell checking identifies the words that It then compares each word with a known list of are valid in the language as well as misspelled words in the correctly spelled words (i.e. -
A Prototype System for Multilingual Data Discovery of International Long-Term Ecological Research (ILTER) Network Data
Ecological Informatics 40 (2017) 93–101 Contents lists available at ScienceDirect Ecological Informatics journal homepage: www.elsevier.com/locate/ecolinf A prototype system for multilingual data discovery of International Long-Term Ecological Research (ILTER) Network data Kristin Vanderbilt a,⁎,JohnH.Porterb, Sheng-Shan Lu c,NicBertrandd,DavidBlankmane, Xuebing Guo f, Honglin He f, Don Henshaw g, Karpjoo Jeong h, Eun-Shik Kim i,Chau-ChinLinc,MargaretO'Brienj, Takeshi Osawa k, Éamonn Ó Tuama l, Wen Su f, Haibo Yang m a Department of Biology, MSC03 2020, University of New Mexico, Albuquerque, NM 87131, USA b Department of Environmental Sciences, University of Virginia, Charlottesville, VA 22904, USA c Taiwan Forestry Research Institute, 53 Nan Hai Rd., Taipei, Taiwan d Centre for Ecology & Hydrology, Lancaster Environmental Centre, Lancaster LA1 4AP, UK e Jerusalem 93554, Israel f Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China g U.S. Forest Service Pacific Northwest Research Station, Forestry Sciences Laboratory, 3200 SW Jefferson Way, Corvallis, OR 97331, USA h Department of Internet and Multimedia Engineering, Konkuk University, Seoul 05029, Republic of Korea i Kookmin University, Department of Forestry, Environment, and Systems, Seoul 02707, Republic of Korea j Marine Science Institute, University of California, Santa Barbara, CA 93106, USA k National Institute for Agro-Environmental Sciences, Tsukuba, Ibaraki 305-8604, Japan l GBIF Secretariat, Universitetsparken 15, DK-2100 Copenhagen Ø, Denmark m School of Ecological and Environmental Sciences, East China Normal University, 500 Dongchuan Rd., Shanghai 200241, China article info abstract Article history: Shared ecological data have the potential to revolutionize ecological research just as shared genetic sequence Received 6 September 2016 data have done for biological research. -
Selecting Target Datasets for Semantic Enrichment
Task Force on Enrichment and Evaluation 29/10/2015 Selecting target datasets for semantic enrichment Editors Antoine Isaac, Hugo Manguinhas, Valentine Charles (Europeana Foundation R&D) and Juliane Stiller (Max Planck Institute for the History of Science) 1. INTRODUCTION 2 2. METHODS FOR ANALYZING AND SELECTING DATASETS FOR SEMANTIC ENRICHMENT 2 2.1. Analyse your source data 3 2.2. Identify your requirements 4 2.3. Find your targets 5 2.4. Select your targets 6 2.4.1. Availability 6 2.4.2. Access 6 2.4.3. Granularity and Coverage 6 2.4.4. Quality 9 2.4.5. Connectivity 10 2.4.6. Size 10 2.5. Test the selected target 14 3. BUILDING A NEW TARGET FOR ENRICHMENT 14 3.1. Refining existing targets to build a new target 14 3.1.1. Building a classification for Europeana Food and Drink 15 3.1.2. Building a vocabulary for First World War in Europeana 1914-1918 15 3.2. Building a new target from scratch 15 REFERENCES 16 1/17 Task Force on Evaluation and Enrichment – Selecting target datasets for semantic enrichment 1. Introduction As explained in the main report one of the components of the enrichment process is the target vocabulary. The selection of the target against which the enrichment will be performed, being a dataset of cultural objects or a specific knowledge organization system, needs to be carefully done. In many cases the selection of the appropriate target for the source data will determine the quality of the enrichments. The Task Force recommends users to re-use existing targets when performing enrichments as it increases interoperability between datasets and reduces redundancies between the different targets1. -
Mountain Ranges in India for Banking & SSC Exam
Mountain Ranges in India for Banking & SSC Exam - GK Notes in PDF Every year 11th December is observed as the 'International Mountain Day.' A theme is set for this day to mark a purpose and to create awareness. This year's theme is “#MountainsMatter.” It will highlight the importance of mountains and their need for youth, water, disaster risk reduction, food, indigenous peoples and biodiversity. Mountains play a pivotal role in our life by altering the weather pattern and climatic conditions. They are also rich in endemic species and has great impact on Natural Ecosystem of the country. Thus, the knowledge of Mountain Ranges is very important from the point of view of various Banking, SSC and other Government Exams. To help you prepare this topic, here's the account of the major Mountain Ranges in India. A Mountain Range is a sequential chain or series of mountains or hills with similarity in form, structure and alignment that have arisen from the same cause, usually an orogeny. List of the prominent Mountain Ranges in India ⇒ The Himalaya Range • Himalaya is the highest mountain ranges in India • The word Himalaya literally translates to "abode of snow" from Sanskrit. • The Himalayan Mountain range is the youngest mountain range of India and new fold mountain is formed by the collision of two tectonic plates. • Himalayan Mountain Range has almost every highest peak of the world. • On an average they have more than 100 peaks with height more than 7200 m. 1 | P a g e • Nanga Parbat and Namcha Barwa are considered as the western and eastern points of the Himalaya. -
Finite State Recognizer and String Similarity Based Spelling
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by BRAC University Institutional Repository FINITE STATE RECOGNIZER AND STRING SIMILARITY BASED SPELLING CHECKER FOR BANGLA Submitted to A Thesis The Department of Computer Science and Engineering of BRAC University by Munshi Asadullah In Partial Fulfillment of the Requirements for the Degree of Bachelor of Science in Computer Science and Engineering Fall 2007 BRAC University, Dhaka, Bangladesh 1 DECLARATION I hereby declare that this thesis is based on the results found by me. Materials of work found by other researcher are mentioned by reference. This thesis, neither in whole nor in part, has been previously submitted for any degree. Signature of Supervisor Signature of Author 2 ACKNOWLEDGEMENTS Special thanks to my supervisor Mumit Khan without whom this work would have been very difficult. Thanks to Zahurul Islam for providing all the support that was required for this work. Also special thanks to the members of CRBLP at BRAC University, who has managed to take out some time from their busy schedule to support, help and give feedback on the implementation of this work. 3 Abstract A crucial figure of merit for a spelling checker is not just whether it can detect misspelled words, but also in how it ranks the suggestions for the word. Spelling checker algorithms using edit distance methods tend to produce a large number of possibilities for misspelled words. We propose an alternative approach to checking the spelling of Bangla text that uses a finite state automaton (FSA) to probabilistically create the suggestion list for a misspelled word. -
Spell Checking in Computer-Assisted Language Learning: a Study of Misspellings by Nonnative Writers of German
SPELL CHECKING IN COMPUTER-ASSISTED LANGUAGE LEARNING: A STUDY OF MISSPELLINGS BY NONNATIVE WRITERS OF GERMAN Anne Rimrott Diplom, Justus Liebig Universitat, 2002 THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS In the Department of Linguistics O Anne Rimrott 2005 SIMON FRASER UNIVERSITY Spring 2005 All rights reserved. This work may not be reproduced in whole or in part, by photocopy or other means, without permission of the author. APPROVAL Name: Anne Rimrott Degree: Master of Arts Title of Thesis: Spell Checking in Computer-Assisted Language Learning: A Study of Misspellings by Nonnative Writers of German Examining Committee: Chair: Dr. Alexei Kochetov Assistant Professor, Department of Linguistics Dr. Trude Heift Senior Supervisor Associate Professor, Department of Linguistics Dr. Chung-hye Han Supervisor Assistant Professor, Department of Linguistics Dr. Maria Teresa Taboada Supervisor Assistant Professor, Department of Linguistics Dr. Mathias Schulze External Examiner Assistant Professor, Department of Germanic and Slavic Studies University of Waterloo Date DefendedIApproved: SIMON FRASER UNIVERSITY PARTIAL COPYRIGHT LICENCE The author, whose copyright is declared on the title page of this work, has granted to Simon Fraser University the right to lend this thesis, project or extended essay to users of the Simon Fraser University Library, and to make partial or single copies only for such users or in response to a request from the library of any other university, or other educational institution, on its own behalf or for one of its users. The author has further granted permission to Simon Fraser University to keep or make a digital copy for use in its circulating collection. -
Exploiting Wikipedia Semantics for Computing Word Associations
Exploiting Wikipedia Semantics for Computing Word Associations by Shahida Jabeen A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science. Victoria University of Wellington 2014 Abstract Semantic association computation is the process of automatically quan- tifying the strength of a semantic connection between two textual units based on various lexical and semantic relations such as hyponymy (car and vehicle) and functional associations (bank and manager). Humans have can infer implicit relationships between two textual units based on their knowledge about the world and their ability to reason about that knowledge. Automatically imitating this behavior is limited by restricted knowledge and poor ability to infer hidden relations. Various factors affect the performance of automated approaches to com- puting semantic association strength. One critical factor is the selection of a suitable knowledge source for extracting knowledge about the im- plicit semantic relations. In the past few years, semantic association com- putation approaches have started to exploit web-originated resources as substitutes for conventional lexical semantic resources such as thesauri, machine readable dictionaries and lexical databases. These conventional knowledge sources suffer from limitations such as coverage issues, high construction and maintenance costs and limited availability. To overcome these issues one solution is to use the wisdom of crowds in the form of collaboratively constructed knowledge sources. An excellent example of such knowledge sources is Wikipedia which stores detailed information not only about the concepts themselves but also about various aspects of the relations among concepts. The overall goal of this thesis is to demonstrate that using Wikipedia for computing word association strength yields better estimates of hu- mans’ associations than the approaches based on other structured and un- structured knowledge sources. -
Words in a Text
Cross-lingual geo-parsing for non-structured data Judith Gelernter Wei Zhang Language Technologies Institute Language Technologies Institute School of Computer Science School of Computer Science Carnegie Mellon University Carnegie Mellon University [email protected] [email protected] ABSTRACT to cross-language geo-information retrieval. We will examine A geo-parser automatically identifies location words in a text. We Strötgen’s view that normalized location information is language- have generated a geo-parser specifically to find locations in independent [16]. Difficulties in cross-language experiments are unstructured Spanish text. Our novel geo-parser architecture exacerbated when the languages use different alphabets, and when combines the results of four parsers: a lexico-semantic Named the focus is on proper names, as in this case, the names of Location Parser, a rules-based building parser, a rules-based street locations. parser, and a trained Named Entity Parser. Each parser has Geo-parsing structured versus unstructured text requires different different strengths: the Named Location Parser is strong in recall, language processing tools. Twitter messages are challenging to and the Named Entity Parser is strong in precision, and building geo-parse because of their non-grammaticality. To handle non- and street parser finds buildings and streets that the others are not grammatical forms, we use a Twitter tokenizer rather than a word designed to do. To test our Spanish geo-parser performance, we tokenizer. We use an English part of speech tagger created for compared the output of Spanish text through our Spanish geo- tweets, which was not available to us in Spanish. -
Automatic Spelling Correction Based on N-Gram Model
International Journal of Computer Applications (0975 – 8887) Volume 182 – No. 11, August 2018 Automatic Spelling Correction based on n-Gram Model S. M. El Atawy A. Abd ElGhany Dept. of Computer Science Dept. of Computer Faculty of Specific Education Damietta University Damietta University, Egypt Egypt ABSTRACT correction. In this paper, we are designed, implemented and A spell checker is a basic requirement for any language to be evaluated an end-to-end system that performs spellchecking digitized. It is a software that detects and corrects errors in a and auto correction. particular language. This paper proposes a model to spell error This paper is organized as follows: Section 2 illustrates types detection and auto-correction that is based on n-gram of spelling error and some of related works. Section 3 technique and it is applied in error detection and correction in explains the system description. Section 4 presents the results English as a global language. The proposed model provides and evaluation. Finally, Section 5 includes conclusion and correction suggestions by selecting the most suitable future work. suggestions from a list of corrective suggestions based on lexical resources and n-gram statistics. It depends on a 2. RELATED WORKS lexicon of Microsoft words. The evaluation of the proposed Spell checking techniques have been substantially, such as model uses English standard datasets of misspelled words. error detection & correction. Two commonly approaches for Error detection, automatic error correction, and replacement error detection are dictionary lookup and n-gram analysis. are the main features of the proposed model. The results of the Most spell checkers methods described in the literature, use experiment reached approximately 93% of accuracy and acted dictionaries as a list of correct spellings that help algorithms similarly to Microsoft Word as well as outperformed both of to find target words [16]. -
Catalogue 1986
PART Β' DOCUMENTS * » * OFFICE FOR OFFICIAL PUBLICATIONS OF THE EUROPEAN COMMUNITIES PART Β: DOCUMENTS * ob ** OFFICE FOR OFFICIAL PUBLICATIONS OF THE EUROPEAN COMMUNITIES This publication is also available in the following languages: ES ISBN 92-825-6769-9 DA ISBN 92-825-6770-2 DE ISBN 92-825-6771-0 GR ISBN 92-825-6772-9 FR ISBN 92-825-6774-5 IT ISBN 92-825-6775-3 NL ISBN 92-825-6776-1 PT ISBN 92-825-6777-X Luxembourg: Office for Officiai Publications of the European Communities. 1987 ISBN 92-825-6773-7 Catalogue number: FX-46-86-315-EN-C Printed in Luxembourg Introduction The annual catalogue of publications of the European nology), is based mainly on analysis and subsequent Communities is divided into two parts. redefinition of the document using keywords or key expressions contained in the EUROVOC thesaurus. Part A comprises the monographs and series pub This thesaurus will be supplied to subscribers or lished by the Institutions of the European Communi users on request. ties as well as the yearly periodicals. The index contains from one to five keywords for Part Β comprises the bibliographic notices of the each document, set out in alphabetical order with as COM Documents, EP Reports and ESC Opinions many permutations as there are keywords. This published during the year on paper and microfiche, makes for easier searches. The keywords are fol by the Institutions of the European Communities. lowed by a reference number. This number refers to the sequence number in the catalogue (see detailed This makes it easier for users to follow the progress explanation on page 5). -
Development of a Persian Syntactic Dependency Treebank
Development of a Persian Syntactic Dependency Treebank Mohammad Sadegh Rasooli Manouchehr Kouhestani Amirsaeid Moloodi Department of Computer Science Department of Linguistics Department of Linguistics Columbia University Tarbiat Modares University University of Tehran New York, NY Tehran, Iran Tehran, Iran [email protected] [email protected] [email protected] Abstract tions in tasks such as machine translation. Depen- dency treebanks are collections of sentences with This paper describes the annotation process their corresponding dependency trees. In the last and linguistic properties of the Persian syn- decade, many dependency treebanks have been de- tactic dependency treebank. The treebank veloped for a large number of languages. There are consists of approximately 30,000 sentences at least 29 languages for which at least one depen- annotated with syntactic roles in addition to morpho-syntactic features. One of the unique dency treebank is available (Zeman et al., 2012). features of this treebank is that there are al- Dependency trees are much more similar to the hu- most 4800 distinct verb lemmas in its sen- man understanding of language and can easily rep- tences making it a valuable resource for ed- resent the free word-order nature of syntactic roles ucational goals. The treebank is constructed in sentences (Kubler¨ et al., 2009). with a bootstrapping approach by means of available tagging and parsing tools and man- Persian is a language with about 110 million ually correcting the annotations. The data is speakers all over the world (Windfuhr, 2009), yet in splitted into standard train, development and terms of the availability of teaching materials and test set in the CoNLL dependency format and annotated data for text processing, it is undoubt- is freely available to researchers.