Views
4 years ago

Automatic Extraction of Examples for Word Sense Disambiguation

Automatic Extraction of Examples for Word Sense Disambiguation

CONTENTS 5 4.3

CONTENTS 5 4.3 Senseval-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.4 Senseval-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.5 Senseval-3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.5.1 The All-Words Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.5.2 The Lexical Sample Task . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.5.3 Other Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.6 Semeval-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5 TiMBL: Tilburg Memory-Based Learner 44 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.2 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 6 Automatic Extraction of Examples for WSD 47 6.1 Overview of the System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 6.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.2.1 Sense Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 6.2.2 Source Corpora . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 6.2.3 Automatic Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 6.3 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 6.3.1 Basic Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 6.3.2 Part-of-Speech Tagging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 6.4 Training and Test Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6.4.1 Feature Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 6.4.2 Training Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.4.3 Test Set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 6.5 Algorithm Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 6.6 Parameter Optimizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.6.1 General Parameter Optimizations . . . . . . . . . . . . . . . . . . . . . . . . 60 6.6.2 Automatic Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 6.7 Scoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.8 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 6.8.1 Supervised WSD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 6.8.2 Unsupervised WSD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6.8.3 Semi-supervised WSD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

CONTENTS 6 6.8.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 6.9 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 7 Conclusion, Future and Related Work 74 7.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 7.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 References 77 A List of Abbreviations 89 B Pool of Features 90 C Training and Test Set (Senseval-3 English Lexical Sample Task) 92 D Graphs 93 E Tables 96

A Machine Learning Approach for Automatic Road Extraction - asprs
Selective Sampling for Example-based Word Sense Disambiguation
Word sense disambiguation with pattern learning and automatic ...
Word Sense Disambiguation Using Automatically Acquired Verbal ...
Using Machine Learning Algorithms for Word Sense Disambiguation ...
Word Sense Disambiguation The problem of WSD - PEOPLE
BOOTSTRAPPING IN WORD SENSE DISAMBIGUATION
Performance Metrics for Word Sense Disambiguation
Word Sense Disambiguation - cs547pa1
WORD SENSE DISAMBIGUATION - Leffa
Word Sense Disambiguation
Word Sense Disambiguation Using Selectional Restriction - Ijsrp.org
MRD-based Word Sense Disambiguation - the Association for ...
word sense disambiguation and recognizing textual entailment with ...
Using Lexicon Definitions and Internet to Disambiguate Word Senses
KU: Word Sense Disambiguation by Substitution - Deniz Yuret's ...
A Comparative Evaluation of Word Sense Disambiguation Algorithms
Using unsupervised word sense disambiguation to ... - INESC-ID
Semi-supervised Word Sense Disambiguation ... - ResearchGate
Word Sense Disambiguation: An Empirical Survey - International ...
Word Sense Disambiguation is Fundamentally Multidimensional
Using Meaning Aspects for Word Sense Disambiguation
Word-Sense Disambiguation for Machine Translation
Unsupervised learning of word sense disambiguation rules ... - CLAIR
Word Sense Disambiguation Using Association Rules: A Survey
Similarity-based Word Sense Disambiguation
Word Sense Disambiguation with Pictures - CiteSeerX
Ch7. word sense disambiguation
Word Sense Disambiguation with Pictures - CLAIR