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langid.py for better language modelling Paul Cook and Marco Lui 107 LaBB-CAT: an Annotation Store Robert Fromont and Jennifer Hay 113 Classification of Study Region in Environmental Science Abstracts Jared Willett, Timothy Baldwin, David Martinez and Angus Webb 118 ALTA Shared Task papers 123 Overview of the ALTA 2012 Shared Task Iman Amini, David Martinez and Diego Molla 124 Automatic sentence classifier using sentence ordering features for Event Based Medicine: Shared task system description Spandana Gella and Duong Thanh Long 130 Feature Stacking for Sentence Classification in Evidence-Based Medicine Marco Lui 134 Experiments with Clustering-based Features for Sentence Classification in Medical Publications: Macquarie Test’s participation in the ALTA 2012 shared task. Diego Mollá 139 VIII
Invited talks 1
Page 1 and 2: Australasian Language Technology As
Page 3 and 4: ALTA 2012 Workshop Committees Works
Page 5 and 6: ALTA 2012 Programme The proceedings
Page 7: Contents Invited talks 1 Using a la
Page 11 and 12: Diverse Words, Shared Meanings: Sta
Page 13 and 14: A Citation Centric Annotation Schem
Page 15 and 16: The structure of this paper is as f
Page 17 and 18: understanding of sentences surround
Page 19 and 20: henceforth referred to as Annotator
Page 21 and 22: References Angrosh, M A, Cranefield
Page 23 and 24: Semantic Judgement of Medical Conce
Page 25 and 26: 2012). The TE model provides a form
Page 27 and 28: Correlation coefficient with human
Page 29 and 30: Pair # Concept 1 Doc. Freq. Concept
Page 31 and 32: Active Learning and the Irish Treeb
Page 33 and 34: 2.2 Sources of annotator disagreeme
Page 35 and 36: complement (csubj) 2 . See Figure 4
Page 37 and 38: top Y trees from this ordered set a
Page 39 and 40: References Ron Artstein and Massimo
Page 41 and 42: Unsupervised Estimation of Word Usa
Page 43 and 44: not lend itself to determining unsu
Page 45 and 46: T 0: think, want, thing, look, tell
Page 47 and 48: correlation is much stronger than t
Page 49 and 50: References Eneko Agirre and Philip
Page 51 and 52: eaders with sentences with a negati
Page 53 and 54: Question Which word is closest in m
Page 55 and 56: Acceptability and negativity: conce
Page 57 and 58: MORE NEGATIVE / LESS NEGATIVE MR Δ
Page 59 and 60:
Julie Elizabeth Weeds. 2003. Measur
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cation produced by a supervised mac
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understood to carry a lower weight
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Figure 1: Average classification ac
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0.8 0.75 0.7 AuthorA4 AuthorB4 Auth
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Segmentation and Translation of Jap
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tomatosōsu “tomato sauce”, rev
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“markup” and “Mach”, so the
Page 75 and 76:
MWE Segmentation Possible Translati
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English Term Pairs from Search Engi
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techniques used are, of necessity,
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ation metrics robust, if they are v
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Figure 2: Methodologies of human as
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an optimal method? Machine translat
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Towards Two-step Multi-document Sum
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archical and non-hierarchical relat
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We use the MetaMap 7 tool to automa
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T T & C CC z -1.5 -1.27 -1.33 p-val
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References Alan R. Aronson. 2001. E
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techniques and results are shown. F
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Rock Hip-Hop Electronic Punk Metal
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Rank Frequency Frequency (filtered)
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Ex. 1 Ex. 2 Ex. 3 Score Song Title
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Free-text input vs menu selection:
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consistent with the much earlier ed
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3.2 Dialogue system architecture Th
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Control Free-text Menu-based n=119
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tions in analyzing algebra example
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langid.py for better language model
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documents of MIME type text/html wi
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Acknowledgments NICTA is funded by
Page 121 and 122:
LaBB-CAT: an Annotation Store Rober
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elationship between the coincidence
Page 125 and 126:
Communication 33 (special issue on
Page 127 and 128:
matically extracting location infor
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Classifier DBp:1R Geo:1R D+G:1R DBp
Page 131 and 132:
ALTA Shared Task papers 123
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All Struct. Unstruct. Total - Abstr
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System Population Intervention Back
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tence positions (absolute and relat
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sitional attributes, and sequential
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ordering labels, All includes BOW,
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3 Software Used All experimentation
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Combination Output Public Private C
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Experiments with Clustering-based F
Page 149 and 150:
3 Results For the initial experimen
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