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CS4740 (Spring 2015!) Intro. to Natural Language Processing POS Tagging Jon Park DEPARTMENT OF COMPUTER SCIENCE CORNELL UNIVERSITY * Adopted/modified from Cardie(Cornell) & Lin(UMD)’s presentations. Source: Calvin and Hobbs Quick check on iClickerGO A. I’m using the iClickerGO App! B. I’m using an iClicker! Agenda  What are parts of speech (POS)?  What is POS tagging? Parts of Speech  “Equivalence class” of linguistic entities  “Categories” or “types” of words  Study dates back to the ancient Greeks  Dionysius Thrax of Alexandria (c. 100 BC)  8 parts of speech: noun, verb, pronoun, preposition, adverb, conjunction, participle, article  Remarkably enduring list!  Typically much larger nowadays:  Penn Treebank: 45  Brown corpus: 87  C7 tagset: 146 5 How do we define POS?  By meaning (can be unreliable)  Verbs are actions  Adjectives are properties  Nouns are things  By the syntactic environment  What occurs nearby?  What does it act as?  By what morphological processes affect it  What affixes does it take?  Combination of the above Parts of Speech  Open class  Impossible to completely enumerate  New words continuously being invented, borrowed, etc.  Closed class  Closed, fixed membership  Reasonably easy to enumerate  Generally, short function words that “structure” sentences Open Class POS  Four major open classes in English     Nouns Verbs Adjectives Adverbs  All languages have nouns and verbs... but may not have the other two Open Class POS  Nouns  Semantics:  Generally, words for people, places, things  But not always (bandwidth, energy, ...)  Syntactic environment:  Occurring with determiners  Pluralizable, possessivizable  Other characteristics:  Mass vs. count nouns Open Class POS  Verbs  Semantics:  Generally, denote actions, processes, etc.  Syntactic environment:  Intransitive, transitive, ditransitive  Alternations  Other characteristics:  Main vs. auxiliary verbs  Gerunds (verbs behaving like nouns)  Participles (verbs behaving like adjectives) Open Class POS  Adjectives  Generally modify nouns, e.g., tall girl  Adverbs  Sometimes modify verbs, e.g., sang beautifully  Sometimes modify adjectives, e.g., extremely hot Closed Class POS  Prepositions  In English, occurring before noun phrases  Specifying some type of relation (spatial, temporal, …)  Examples: on the shelf, before noon  Particles  Resembles a preposition, but combined with a verb (“phrasal verbs”)  Examples: find out, turn over, go on Closed Class POS  Determiners  Establish reference for a noun  Examples: a, an, the (articles), that, this, many, such, …  Pronouns  Refer to person or entities: he, she, it  Possessive pronouns: his, her, its  Wh-pronouns: what, who Closed Class POS  Conjunctions  Coordinating conjunctions  Join two elements of “equal status”  Examples: cats and dogs, salad or soup  Subordinating conjunctions  Join two elements of “unequal status”  Examples: We’ll leave after you finish eating. While I was waiting in line, I saw my friend.  Complementizers are a special case: I think that you should finish your assignment Agenda  What are parts of speech (POS)?  What is POS tagging? Part of speech tagging  Assign the correct part of speech (word class) to each word/token in a document “The/DT planet/NN Jupiter/NNP and/CC its/PPS moons/NNS are/VBP in/IN effect/NN a/DT mini-solar/JJ system/NN ,/, and/CC Jupiter/NNP itself/PP is/VBZ often/RB called/VBN a/DT star/NN that/IN never/RB caught/VBN fire/NN ./.”  Needed as an initial processing step for a number of language technology applications  Answer extraction in Question Answering systems  Base step in identifying syntactic phrases for IR systems  Critical for word-sense disambiguation  Information extraction  … Why is POS tagging hard?  Ambiguity      He will race/VB the car. When will the race/NOUN end? The boat floated/ VBD. The boat floated/ VBD down Fall Creek. The boat floated/VBN down Fall Creek sank.  Average of ~2 parts of speech for each word  The number of tags used by different systems varies a lot. Some systems use < 20 tags, while others use > 400. POS Tagging is Hard (Even for Humans)  particle vs. preposition    past tense vs. past participle    The horse walked past the barn. The horse walked past the barn fell. noun vs. adjective?    He talked over the deal. He talked over the telephone. My favorite color is red. The car is red. gerund vs. present participle   Fishing can be fun. I am fishing. To obtain gold standards for evaluation, annotators rely on a set of From Ralph Grishman, NYU tagging guidelines. Penn Treebank Tagset POS Tagging Example It is a nice night. It/PP is/VBZ a/DT nice/JJ night/NN ./. POS Tagging Example Nobody ever takes the newspapers she sells. Nobody/NN ever/RB takes/VBZ the/DT newspapers/NNS she/PP sells/VBZ ./. POS Tagging Exercise He is a tall, skinny guy with a long, sad, mean-looking kisser, and a mournful voice. He/PP is/VBZ a/DT tall/JJ ,/, skinny/JJ guy/NN with/IN a/DT long/JJ ,/, sad/JJ ,/, mean-looking/JJ kisser/NN ,/, and/CC a/DT mournful/JJ voice/NN ./. POS Tagging Which of the following has been correctly tagged? A. I am sitting in Mindy’s restaurant eating the gefillte fish. B. I am sitting in Mindy’s restaurant eating the gefillte fish. C. I am sitting in Mindy’s restaurant eating the gefillte fish. POS Tagging Exercise I am sitting in Mindy’s restaurant eating the gefillte fish. I/PP am/VBP sitting/VBG in/IN Mindy/NNP ’s/POS restaurant/NN eating/VBG the/DT gefillte/NN fish/NN ./. a ship shipping ship shipping shipping ships Think buffalo buffalo buffalo buffalo buffalo buffalo buffalo buffalo buffalo. Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo. Buffalo buffalo, Buffalo buffalo buffalo, buffalo Buffalo buffalo. n1. the city of Buffalo, NY n2. an animal…the American bison v. to bully, confuse, deceive, or intimidate
 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
									 
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                            