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Automatic Extraction of Examples for Word Sense Disambiguation

Automatic Extraction of Examples for Word Sense Disambiguation

List of

List of Tables 2.1 Basic approaches to WSD as in (Agirre and Edmonds, 2007). . . . . . . . . . . . . . 13 2.2 Performance and short description for the unsupervised systems participating in the SENSEVAL-3 English lexical sample task. Precision (P) and recall (R) (see Section 4.1) figures are provided for both fine-grained and coarse-grained scoring (Mihalcea et al., 2004a). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.3 Performance and short description of the supervised systems participating in the SENSEVAL-3 English lexical sample Word Sense Disambiguation task. Precision (P) and recall (R) (see Section 4.1) figures are provided for both fine-grained and coarse-grained scoring (Mihalcea et al., 2004a). . . . . . . . . . . . . . . . . . . . . . 19 2.4 Feature vectors for the sentences in (2). . . . . . . . . . . . . . . . . . . . . . . . . . 24 2.5 Supervised word sense disambiguation algorithms. . . . . . . . . . . . . . . . . . . 26 4.1 Summary of the Senseval-1 evaluation task. . . . . . . . . . . . . . . . . . . . . . . 38 4.2 Summary of the Senseval-2 evaluation task. . . . . . . . . . . . . . . . . . . . . . . 39 4.3 Summary of the sense inventory of the words present in the English lexical sample task (Mihalcea et al., 2004a). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.4 Summary of the Senseval-3 Lexical Sample Task. . . . . . . . . . . . . . . . . . . . 42 6.1 The collection and size of lexicon examples used in our system. . . . . . . . . . . . 51 6.2 The collection and size of corpora examples used in our system. . . . . . . . . . . . 52 6.3 Features included in the feature vectors of our system and their corresponding values from our toy example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.4 The total collection of examples used in our system. . . . . . . . . . . . . . . . . . . 59 6.5 System performance on manually annotated data without optimization of the k parameter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.6 System performance on manually annotated data with optimization of the k pa- rameter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.7 Comparison with the three best supervised systems in the Senseval-3 lexical sam- ple task (Mihalcea et al., 2004a). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 7

LIST OF TABLES 8 6.8 System performance on automatically annotated data without optimization of the k parameter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.9 System performance on automatically annotated data with optimization of the k parameter. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.10 Comparison with the four best unsupervised systems in the Senseval-3 lexical sample task (Mihalcea et al., 2004a). . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 6.11 System performance on automatically annotated data without optimization of the k parameter and filtered for preservation of the distribution of the senses as in the original manually annotated training set. . . . . . . . . . . . . . . . . . . . . . . . . 67 6.12 System performance on automatically annotated data with optimization of the k parameter and filtered for preservation of the distribution of the senses as in the original manually annotated training set. . . . . . . . . . . . . . . . . . . . . . . . . 68 6.13 System performance on automatically annotated data without optimization of the k parameter and distance and distribution filtering mixed with the manually an- notated training set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.14 System performance on automatically annotated data with optimization of the k parameter and distance and distribution filtering mixed with the manually anno- tated training set. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.15 Summary of the results for all experiments. . . . . . . . . . . . . . . . . . . . . . . . 72 6.16 Comparison of our system’s scores with the upper and lower bound for the exper- iment and also with the best performing system in the Senseval-3 English lexical sample task. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 7.1 The performance of our system on the Senseval-3 Lexical Sample Task data. . . . 97 7.2 The performance of our system trained on the automatically gathered examples and tested on the provided by Senseval-3 Lexical Sample Task test set. . . . . . . . 98 7.3 System performance on automatically annotated data with optimization of the k parameter and filtered for preservation of the distribution of the senses as in the original manually annotated training set. . . . . . . . . . . . . . . . . . . . . . . . . 99 7.4 System performance on automatically annotated data with optimization of the k parameter and distance filtering mixed with the manually annotated training set. 100

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