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Text Characteristics of Clinical Reports and Their Implications on the Readability of Per- sonal Health Records
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In: http://consumerhealthvocab.org/docs/medinfo07_Zeng.pdf
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Vocabulary Development in English and Chinese: A Comparative Study with Self-Organizing Neural Networks
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In: http://csjarchive.cogsci.rpi.edu/Proceedings/2008/pdfs/p1900.pdf
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Wireless: Some Facts and Figures from a Corpus-driven Study
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In: http://dialnet.unirioja.es/descarga/articulo/3156503.pdf
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64 |
Simulating Word Associations in an L2: Approaches to Lexical Organisation
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In: http://digitum.um.es/xmlui/bitstream/10201/2573/1/2579871.pdf
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65 |
Glossing, Inferencing, and Incidental Vocabulary Learning
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In: http://faculty.ksu.edu.sa/aljarf/documents/english language teaching conference - iran 2008/shima ghahari and meissam heidarolad.pdf
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66 |
Restoring accents in unknown biomedical words: application to the French MeSH thesaurus
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In: http://www-new.biomath.jussieu.fr/~pz/FTPapiers/Zweigenbaum_IJMI2002.pdf
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Abstract:
In languages with diacritic marks, such as French, there remain instances of textual or terminological resources that are available in electronic form without diacritic marks, which hinders their use in natural language interfaces. In a specialized domain such as medicine, it is often the case that some words are not found in the available electronic lexicons. The issue of accenting unknown words then arises: it is the theme of this work. We propose two internal methods for accenting unknown words, which both learn on a reference set of accented words the contexts of occurrence of the various accented forms of a given letter. One method is adapted from part-of-speech tagging, the other is based on finite state transducers. We show experimental results for letter e on the French version of the Medical Subject Headings thesaurus. With the best training set, the tagging method obtains a precision-recall breakeven point of 84.29/4.4 % and the transducer method 83.89/4.5 % (with a baseline at 64%) for the unknown words that contain this letter. A consensus combination of both increases precision to 92.09/3.7 % with a recall of 75%. We perform an error analysis and discuss further steps that might help improve over the current performance.
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Keyword:
Algorithms; Controlled vocabulary; France; Language; Machine learning; Natural language processing
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.493.5631 http://www-new.biomath.jussieu.fr/~pz/FTPapiers/Zweigenbaum_IJMI2002.pdf
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67 |
Specific Stylistic Features of English for Electrical Engineering
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In: http://www.ineer.org/events/iceer2004/proceedings/papers/1587.pdf
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68 |
Porting Persian Lexical Resources to NooJ. 14
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In: http://www.c-s-p.org/Flyers/978-1-4438-4733-9-sample.pdf
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INTERSPEECH 2013 Feature-rich sub-lexical language models using a maximum entropy approach
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In: http://www-i6.informatik.rwth-aachen.de/publications/download/874/BashaShaikM.AliEl-DesokyMousaAmrSchluterRalfNeyHermann--Feature-richsub-lexicallanguagemodelsusingamaximumentropyapproachforGermanLVCSR--2013.pdf
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70 |
MAKING HEALTH COMMUNICATION ACCESSIBLE: A RHETORICAL ANALYSIS OF RADIO HEALTH TALK
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In: http://www.language-and-society.org/journal/2-1/04_sarfo.pdf
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71 |
Political Vocabulary
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In: http://www.trojina.si/elex2011/Vsebine/proceedings/eLex2011-24.pdf
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72 |
ONLINE STRATEGY INSTRUCTION FOR INTEGRATING DICTIONARY SKILLS AND LANGUAGE AWARENESS
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In: http://llt.msu.edu/issues/june2013/ranalli.pdf
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73 |
177 Developing a Vocabulary Learning System with Near-Synonyms and Similar-Form Words
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In: http://www.apsce.net/icce2008/contents/proceeding_0177.pdf
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74 |
Word Sense Disambiguation for Vocabulary Learning
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In: http://www.cs.cmu.edu/~callan/Papers/its08-kulkarni.pdf
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75 |
A Fast Input Method for Tibetan Based on Word in Unicode
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In: http://www.iaeng.org/publication/IMECS2008/IMECS2008_pp374-377.pdf
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76 |
W 2 ANE: When Words Are Not Enough Online Multimedia Language Assistant for People with Aphasia
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In: http://www.cs.princeton.edu/aphasia/papers/ACMMM09-sp20493-ma.pdf
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Sociolinguistics: Speech Communication: *Syntax
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In: http://files.eric.ed.gov/fulltext/ED198699.pdf
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78 |
A Neural-Linguistic Approach for the Recognition of a Wide Arabic Word Lexicon
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In: http://www.loria.fr/%7Eabelaid/publis/Imen-DRR2010.pdf
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79 |
The automation of Directory Assistance Services
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In: http://homepages.inf.ed.ac.uk/kgeorgil/papers/georgila_ijst02.pdf
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Hierarchies in Dictionary Definition Space
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In: http://snap.stanford.edu/nipsgraphs2009/papers/picard-paper.pdf
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