Automatic Mapping Clinical Notes to Medical - RMIT University

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

Program Chairs: Committees Lawrence Cavedon (National ICT Australia and RMIT University, Australia) Ingrid Zukerman (Monash University) Local Chair: Rolf Schwitter (Macquarie University) Program Committee: iv
Full Papers Table of Contents Efficient Combinatory Categorial Grammar Parsing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Bojan Djordjevic, James R. Curran Improved Default Sense Selection for Word Sense Disambiguation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Tobias Hawker, Matthew Honnibal Experiments with Sentence Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Anthony Khoo, Yuval Marom, David Albrecht Computational Semantics in the Natural Language Toolkit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Ewan Klein Classifying Speech Acts using Verbal Response Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Andrew Lampert, Robert Dale, Cecile Paris Word Relatives in Context for Word Sense Disambiguation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 David Martinez, Eneko Agirre, Xinglong Wang Named Entity Recognition for Question Answering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Diego Molla, Menno van Zaanen, Daniel Smith Named Entity Recognition for Astronomy Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Tara Murphy, Tara McIntosh, James R. Curran Die Morphologie (f): Targeted Lexical Acquisition for Languages other than English . . . . . . . . . . . . . . 1 Jeremy Nicholson, Timothy Baldwin, Phil Blunsom Automatic Mapping Clinical Notes to Medical Terminologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Jon Patrick, Yefeng Wang, Peter Budd Pseudo Relevance Feedback Using Named Entities for Question Answering . . . . . . . . . . . . . . . . . . . . . . 1 Luiz Augusto Pizzato, Diego Molla, Cecile Paris Evidence for Gradient Salience: What Happens with Competing Non-salient Referents during Pronoun Resolution? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Ralph L. Rose Web Readability and Computer-Assisted Language Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Alexandra L. Uitdenbogerd Questions Require an Answer: A Deductive Perspective on Questions and Answers . . . . . . . . . . . . . . . 1 Willemijn Vermaat v
Page 1: Proceeding of the Australasian Lang
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
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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 108 and 109:
low-scoring documents are “Click
Page 110 and 111:
a) b) You must complete at least 9
Page 112 and 113:
uct). In this paper, we will only c
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indicate the categories of substruc
Page 116 and 117:
Argument lowering Dependent geach
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who [u : np] WH(np, s, s/?gq) λP.
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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
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a given word sequence Sorig as a pe
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A problem arises with this scheme w
Page 136 and 137:
Example 1: Original Headline: Europ
Page 138 and 139:
|relations(sentence1) ∩ relations
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2685 True False 1275 Bleu Dep Bleu
Page 142 and 143:
Features Acc. C1-prec. C1-recall C1
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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
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Automatic Mapping Clinical Notes to Medical - RMIT University

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