POS Tagging and Corpus-Based Lexical ... - PAN Localization

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Patch Templates The tagger acquires patches to improve its performance. Patch templates are of the form: • If a word is tagged a and it is in context C, then change that tag to b, or • If a word is tagged a and it has lexical property P, then change that tag to b, or • If a word is tagged a and a word in region R has lexical property P, then change that tag to b. Thurseday, June 23, 2005 POS Tagging PANLC 2005
The Transformation Templates (non-lexicalized) Change tag a to tag b when: (where a, b, z and w are variables over parts-of-speech) • The preceding (following) word is tagged z • The word two before (after) is tagged z • One of the two preceding (following) words is tagged z • One of the three preceding (following) words is tagged z • The preceding word is tagged z and the following word is tagged w • The preceding (following) word is tagged z and the word two before (after) is tagged z Thurseday, June 23, 2005 POS Tagging PANLC 2005
Page 1 and 2: Parts-of-Speech Tagging Rajat Kumar
Page 3 and 4: An Ubiquitous Task Sequence label
Page 5 and 6: Example (Words: Parts-of-Speech Tag
Page 7 and 8: Basic Idea of POS Tagging To as
Page 9 and 10: Linguistic Input: Parts of Speech (
Page 11 and 12: Closed Class Types • Determiners
Page 13 and 14: Open Class Types Nouns (Common
Page 15 and 16: Tagset Design An appropriate
Page 17 and 18: Penn Treebank POS Tags 36 + 12 1.
Page 19 and 20: Penn Treebank POS Tags 37. # Pound
Page 21 and 22: Verb forms in English Base VB -S VB
Page 23 and 24: Methods to assign Tags Two common
Page 25 and 26: The ENGTWOL Tagger (Voutilainen, 19
Page 27 and 28: Adverbial-THAT Rule (informal) Giv
Page 29 and 30: Stochastic Taggers Produce an o
Page 31 and 32: Example Probabilities • P(V
Page 33 and 34: Transformation-based Rules Init
Page 35: Two Kinds of Rules Contextual Rul
Page 39 and 40: The Transformation Templates (lexic
Page 41 and 42: Transformations for Unknown Words
Page 43 and 44: Transformation-Based Learning (TBL)
Page 45 and 46: A Rule-based POS Tagger for Hindi
Page 47 and 48: Morphs and Morphemes The decompose
Page 49 and 50: Examples Free Morphemes • morphe
Page 51 and 52: Affixation prefix unknowingly stem
Page 53 and 54: Allomorphy Phonetically Conditione
Page 55 and 56: Inflection Inflectional morphology
Page 57 and 58: Derivational Affixes Some examples
Page 59 and 60: Feature Percolation Convention (FPC
Page 61 and 62: Feature Percolation Convention -2
Page 63 and 64: The Morphology-Syntax Interaction
Page 65 and 66: Morphological Analysis Two basic a
Page 67 and 68: Analytic Principles Principle 2
Page 69 and 70: Analytic Principles Principle 4
Page 71 and 72: Morpheme Analysis DOs • Identify
Page 73 and 74: Morpheme Analysis: Examples The fo
Page 75 and 76: Computational Issues Segmentation
Page 77 and 78: Sample Hindi Morphological Analysis
Page 79 and 80: Shoebox Demo Thurseday, June 23, 2
Page 81 and 82: PNGTAM information in Hindi Verbs
Page 83 and 84: Conclusion A Rule-based POS T
Page 85 and 86: Sources and Suggested Readings Har
Page 87:
Thank You Thurseday, June 23, 2005
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POS Tagging and Corpus-Based Lexical ... - PAN Localization

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