PhD thesis - School of Informatics - University of Edinburgh

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Appendix A. Evaluation Metrics and Notation 179 Annotator A Labels Positive Negative Total Annotator B Positive pApB nApB pB Negative pAnB nAnB nB Total pA nA p+n Table A.2: Contingency table of two annotators. A.2 Inter-annotator Agreement Metrics Inter-annotator agreement (IAA) is calculated by means of a contingency table for the data versions produced by different annotators, e.g. annotators A and B (see Table A.2). This matrix is essentially the same as the one presented in Figure A.1, only that one annotator represents the gold standard and the other the system output. Based on counts in the contingency table, IAA scores can then be computed as several metrics: pairwise accuracy (Acc) and F-score, or the Kappa coefficient (κ). A.2.1 Pairwise accuracy and F-score Brants (2000a), for example, reports IAA for part-of-speech annotation of the German NEGRA treebank in terms of accuracy and F-score. These are also standard metrics used for NER, whereby accuracy is determined on the token level and F-score on the phrasal level. As the English inclusion annotation was carried out on the token level, the IAA F-score is determined on the token level as well: Acc = pApB + nAnB pA + nA 2 ∗ P ∗ R F = P+R = 2 ∗ pA pB pA pB = pApB + nAnB pB + nB PA ∗ pA pB PB PA + pA pB PB (A.6) (A.7) As seen in both equations, accuracy and F-score are symmetric between the test and gold data. Accuracy is symmetric, as it is defined as the ratio of the number of tokens on which both annotators agreed over the total number of tokens. F-score is
Appendix A. Evaluation Metrics and Notation 180 symmetric, as precision(A,B) = recall(B,A) and balanced F-score is the harmonic mean of recall and precision (Brants, 2000a). The annotations of one annotator (A or B) can therefore be arbitrarily chosen as the gold standard reference. A.2.2 Kappa Coefficient While pairwise accuracy and F-score are satisfactory IAA measures, they do not allow a comparison of observed agreement and agreement that occurs completely by chance. An IAA metric that captures this kind of agreement is the kappa coefficient (κ) (Co- hen, 1960). The kappa coefficient is commonly used to determine the IAA of corpus annotations (e.g. Carletta, 1996). It measures the observed agreement between two annotators (po) taking into account agreement that occurs by chance alone, also called the expected agreement (pe): κ = po − pe 1 − pe (A.8) The observed agreement (po), which is essentially accuracy, and the expected agreement (pe) are calculated as follows: po = pApB + nAnB pA + nA = pApB + nAnB pB + nB pe = pA pB nA nB ∗ + ∗ p+n p+n p+n p+n κ-coefficient Strength of agreement < 0.00 Poor 0.00-0.20 Slight 0.21-0.40 Fair 0.41-0.60 Moderate 0.61-0.80 Substantial 0.81-1.00 Almost perfect Table A.3: Agreement interpretation of κ-values (Landis and Koch, 1977). (A.9) (A.10)
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Automatic Detection of English Incl
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these parsers with the annotation-f
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Declaration I declare that this the
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3.3.5 Post-processing Module . . .
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A.2.2 Kappa Coefficient . . . . . .
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5.6 Average relative token frequenc
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3.16 Most frequent English inclusio
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Chapter 1. Introduction 2 siderable
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Chapter 1. Introduction 4 Chapter 3
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Chapter 1. Introduction 6 1.1 Relat
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Chapter 2. Background and Theory 8
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Chapter 3 Tracking English Inclusio
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Chapter 3. Tracking English Inclusi
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Chapter 5 Parsing English Inclusion
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Page 221 and 222: Bibliography 208 Kirkness, A. (1984
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Page 225 and 226: Bibliography 212 Poplack, S. (1988)
Page 227 and 228: Bibliography 214 Sokol, D. K. (2000
Page 229: Bibliography 216 Yang, W. (1990). A
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PhD thesis - School of Informatics - University of Edinburgh

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