Automatic Mapping Clinical Notes to Medical - RMIT University

More documents

Recommendations

Info

$journal of latex class files, vol. 6, no. 1, january ... - RMIT University$

low-scoring documents are “Click on books”, “To view Shockedmovies, you need to have Netscape 2.0 and the Shockwave plug-in”, “Last modified on December 10, 1995”, and “Update Your Profile”. Inserting punctuation may make readability scores more reliable, however, automatic techniques for deciding when to do so are not completely obvious. As mentioned in the introduction, readability measures that take into consideration the use of bulleted lists and headings would be of utility for web page assessment, since these structures are frequently used in web pages, and often are the only textual content within a page. Collins-Thompson and Callan’s approach avoids this issue by using unigram word models exclusively to measure readability (Collins-Thompson and Callan, 2004). However, for the bilingual case, particularly in language pairs such as French and English, this is likely to be ineffective (Uitdenbogerd, 2005). An alternative approach is to filter the pool of web pages to be analysed, either by crawling suitable subdomains, or by applying a set of rules to ensure sufficient suitable text on a page before inclusion. Another important consideration is how interesting the document will be to the user, as a person’s comprehension skills vary with their interest in the text. Indeed, the documents should be sufficiently interesting for users to want to use the proposed system. An existing technique for increasing the chance of interesting documents being presented to the user is collaborative filtering, which relies on user feedback, whether explicit or implicit. Another possibility involves the examination of content words and phrases within documents. 4 CONCLUSIONS The purpose of this preliminary work towards a utility for assisting users in improving foreign language skills via reading, was to find evidence that sufficient documents of suitable readability are likely to exist on the web. We determined the readability of over 90,000 web pages written in English, using the unix style utility and found a considerable range of readability scores. The range of readability scores found in ESL books fell within the lower quartile of web page readability scores, representing 8 to 19% of documents in the collection. This could mean that there are 102 many suitable pages for reading practice which a readability-based reading recommender system could retrieve for users. However, due to the artifacts of web pages and the readability measures, not all pages with low scores in readability are suitable for reading practice. The automated location of those that are suitable is part of the future research plans of this project. An additional factor that must be incorporated is prediction of how interesting the documents are likely to be for users. Our analysis used web pages written in English and compared these to ESL texts under the broad assumption that similar distributions of readability would occur in other languages. However, cultural and political differences of the countries speaking different languages may influence the types of text available, and hence the readability range. Learners of English are relatively fortunate in that there are many reading books specifically written for them. This is not the case for many other languages. It is possible that the Internet may be an even more important reading resource for languages other than English. Acknowledgements We thank Vasundhara Srikantha for her preliminary work on web text analysis. References K. Al-Seghayer. 2001. The effect of multimedia annotation modes on L2 vocabulary acquisition: a comparative study. Language Learning and Technology, 5(1):202–232, January. T. Bell. 2001. Extensive reading: speed and comprehension. The Reading Matrix, 1(1), April. J. R. Bormuth. 1968. Cloze test readability: criterion reference scores. Journal of Educational Measurement, 5:189–196. B. Carter. 2000. Formula for failure. School Library Journal, 46(7):34–37, July. J. S. Chall and E. Dale. 1995. Readability revisited: the new Dale-Chall readability formula. Brookline Books, Massachusetts, USA. B. R. Chiswick. 2004. Linguistic distance: A quantitative measure of the distance between English and other languages. Technical Report 1246, Institute for the Study of Labor (IZA). 2003. Common European framework of reference for languages: Learning, teaching, assessment. http://www.coe.int/T/DG4/ Linguistic/CADRE EN.asp#TopOfPage. Accessed 8 September, 2006.
K. Collins-Thompson and J. Callan. 2004. A language modeling approach to predicting reading difficulty. In Proceedings of the HLT/NAACL 2004 Conference, pages 193–200, Boston. A. Davies. 1984. Simple, simplified and simplification: what is authentic? Applied linguistics and language study, chapter 9, pages 181–198. Longman. S. Ghadirian. 2002. Providing controlled exposure to target vocabulary through the screening and arranging of texts. Language Learning and Technology, 6(1):147–164, January. R. M. Gordon. 1980. The readability of unreadable text. English Journal, pages 60–61, March. J. Higgins. 1983. Can computers teach? Calico Journal, pages 4–6, September. A. P. R. Howatt. 1984. A history of English language teaching. Oxford University Press, Oxford, UK. I. R. Katz and M. I. Bauer. 2001. Sourcefinder: Course preparation via linguistically targeted web search. Educational Technology and Society, 4(3):45–49. M. Kellerman. 1981. The forgotten third skill. Pergamon Press, Oxford. G. R. Klare. 1974. Assessing readability. Reading Research Quarterly, X:62–102. G. Kondrak. 2001. Identifying cognates by phonetic and semantic similarity. In K. Knight, editor, Proceedings of NAACL 2001: 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, volume 2, pages 103–110, Pittsburgh, PA, USA, June. NAACL. G. Krantz. 1991. Learning vocabulary in a foreign language: a study of reading strategies. Ph.D. thesis, University of Göteborg, Sweden. L. L. Lomicka. 1998. “to gloss or not to gloss”: an investigation of reading comprehension online. Language Learning and Technology, 1(2):41–50, January. J. Redish. 2000. Readability formulas have even more limitations than Klare discusses. Journal of Computer Documentation, 24(3):132–137, August. K. A. Schriver. 2000. Readability formulas in the new millennium: What’s the use? Journal of Computer Documentation, 24(3):138–140. S. E. Schwarm and M. Ostendorf. 2005. Reading level assessment using support vector machines and statistical language models. In Proceedings of the Association for Computational Linguistics. L. Si and J. Callan. 2001. A statistical model for scientific readability. In L. Liu and D. Grossman, editors, Proc. International Conference on Information and Knowledge Management, volume 10, pages 574–576, Atlanta, Georgia, USA, November. ACM, ACM. 103 A. L. Uitdenbogerd. 2003. Using the web as a source of graded reading material for language acquisition. In W. Zhou, P. Nicholson, B. Corbitt, and J. Fong, editors, International Conference on Web-based Learning, volume 2783 of Lecture Notes in Computer Science, pages 423–432, Melbourne, Australia, August. Springer. A. L. Uitdenbogerd. 2005. Readability of French as a foreign language and its uses. In A. Turpin and R. Wilkinson, editors, Australasian Document Computing Symposium, volume 10, December. M. West. 1927. The construction of reading material for teaching a foreign language. Dacca University Bulletin, (13). Republished as part of “Teaching English as a foreign language, 1912 – 1936: Pioneers of ELT, Volume V: Towards Carnegie”, R. Smith editor.
Page 1:
Proceeding of the Australasian Lang
Page 4 and 5:
Program Chairs: Committees Lawrence
Page 6 and 7:
Towards the Evaluation of Referring
Page 8 and 9:
The cat ate the hat that I made NP/
Page 10 and 11:
Figure 2: Cells affected by adding
Page 12 and 13:
were used because the accuracy for
Page 14 and 15:
TIME ACC COVER FAIL RATE % secs % %
Page 16 and 17:
model. We explore different estimat
Page 18 and 19:
S.S. Source Sense Source S.S. Acc.
Page 20 and 21:
acy. The difference in correctly as
Page 22 and 23:
Experiments with Sentence Classific
Page 24 and 25:
datasets with skewed distribution t
Page 26 and 27:
words produced by the feature selec
Page 28 and 29:
Sentence Class NB DT SVM Apology 1.
Page 30 and 31:
Computational Semantics in the Natu
Page 32 and 33:
S[sem = ] -> NP[sem=?subj] VP[sem=?
Page 34 and 35:
Any string which cannot be decompos
Page 36 and 37:
satisfy(Formula,model(D,F),G,pos):n
Page 38 and 39:
Classifying Speech Acts using Verba
Page 40 and 41:
The principles are dichotomous —
Page 42 and 43:
ance. Several additional example ut
Page 44 and 45:
our results. In classifying only th
Page 46 and 47:
Word Relatives in Context for Word
Page 48 and 49:
create a collection of training exa
Page 50 and 51:
Query Sense The nave was rebuilt in
Page 52 and 53:
Algorithm Avg S2LS Avg S3LS Avg S2L
Page 54 and 55:
B. Snyder and M. Palmer. 2004. The
Page 56 and 57:
it is even used as a black box that
Page 58 and 59: ecognition in QA, so we will not us
Page 60 and 61: BPER ILOC IPER BLOC BLOC BDATE BLOC
Page 62 and 63: of noise introduced by the addition
Page 64 and 65: 2 Existing annotated corpora Much o
Page 66 and 67: Our FUSE|tel spectrum of HD|sta 738
Page 68 and 69: N Word Correct Tagged 23 OH mol non
Page 70 and 71: L ATEX typesetting information into
Page 72 and 73: in English, neither the determiner
Page 74 and 75: con 4 (Sagot et al., 2006) and Morp
Page 76 and 77: Feature type Positions/description
Page 78 and 79: Miriam Butt, Helge Dyvik, Tracy Hol
Page 80 and 81: ently mostly solved by employing hu
Page 82 and 83: Figure 1: Augmented SCT Lexicon Tok
Page 84 and 85: medical terms can be composed by ad
Page 86 and 87: References Aronson, A. R. (2001). E
Page 88 and 89: technique to most question types, i
Page 90 and 91: User Question Analyser QA System Se
Page 92 and 93: Figure 2: Precision Figure 3: Cover
Page 94 and 95: Analysis and Review. Springer-Verla
Page 96 and 97: 2 Background 2.1 Pronoun Reference
Page 98 and 99: ous accounts of the effects of cohe
Page 100 and 101: Table 1: Overall Results for Object
Page 102 and 103: References Jennifer Arnold, Janet E
Page 104 and 105: In order for appropriate documents
Page 106 and 107: y Michael West. He found that his t
Page 110 and 111: a) b) You must complete at least 9
Page 112 and 113: uct). In this paper, we will only c
Page 114 and 115: indicate the categories of substruc
Page 116 and 117: Argument lowering Dependent geach
Page 118 and 119: who [u : np] WH(np, s, s/?gq) λP.
Page 120 and 121: algorithms, and report the performa
Page 122 and 123: 2.3 Coverage of the Human Data Out
Page 124 and 125: and minimal subsets of the data. Of
Page 126 and 127: output. Finally, and related to the
Page 128 and 129: identifies, to avoid overwhelming t
Page 130 and 131: y the student is first parsed, usin
Page 132 and 133: a given word sequence Sorig as a pe
Page 134 and 135: A problem arises with this scheme w
Page 136 and 137: Example 1: Original Headline: Europ
Page 138 and 139: |relations(sentence1) ∩ relations
Page 140 and 141: 2685 True False 1275 Bleu Dep Bleu
Page 142 and 143: Features Acc. C1-prec. C1-recall C1
Page 144 and 145: Given the lack of research in VSD u
Page 146 and 147: ased features: Type 1 Original sibl
Page 148 and 149: Preposition of the adjunct semantic
Page 150 and 151: Algorithm 1 The algorithm of combin
Page 152 and 153: References Timothy Baldwin and Fran
Page 154 and 155: dependencies in German with respect
Page 156 and 157: wij moeten dit probleem aanpakken w
Page 158 and 159:
a brevity penalty of BLEU n = 1 n =
Page 160 and 161:
plicitly matching the syntax of the
Page 162 and 163:
Probabilities improve stress-predic
Page 164 and 165:
Towards Cognitive Optimisation of a
Page 166 and 167:
Natural Language Processing and XML
Page 168 and 169:
Extracting Patient Clinical Profile
show all

Automatic Mapping Clinical Notes to Medical - RMIT University

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?