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Word Bi-grams and PoS Tags

School of Computing FACULTY OF ENGINEERING . Word Bi-grams and PoS Tags. COMP3310 Natural Language Processing Eric Atwell, Language Research Group (with thanks to Katja Markert, Marti Hearst, and other contributors). Reminder. FreqDist counts of tokens and their distribution can be useful

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Word Bi-grams and PoS Tags

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  1. School of Computing FACULTY OF ENGINEERING Word Bi-grams and PoS Tags COMP3310 Natural Language Processing Eric Atwell, Language Research Group (with thanks to Katja Markert, Marti Hearst, and other contributors)

  2. Reminder • FreqDist counts of tokens and their distribution can be useful • Eg find main characters in Gutenberg texts • Eg compare word-lengths in different languages • Human can predict the next word … • N-gram models are based on counts in a large corpus • Auto-generate a story ... (but gets stuck in local maximum) • Grammatical trends: modal verb distribution predicts genre

  3. Why do puns make us groan? • He drove his expensive car into a tree and found out how the Mercedes bends. Isn't the Grand Canyon just gorges? Time flies like an arrow. Fruit flies like a banana.

  4. Predicting Next Words • One reason puns make us groan is they play on our assumptions of what the next word will be – human language processing involves predicting the most probable next word • They also exploit • homonymy – same sound, different spelling and meaning (bends, Benz; gorges, gorgeous) • polysemy – same spelling, different meaning NLP programs can also make use of word-sequence modeling

  5. Auto-generate a Story How to fix this? Use a random number generator.

  6. Auto-generate a Story The choice() method chooses one item randomly from a list (from random import *)

  7. Part-of-Speech Tagging: Terminology • Tagging • The process of associating labels with each token in a text, using an algorithm to select a tag for each word, eg • Hand-coded rules • Statistical taggers • Brill (transformation-based) tagger • Hybrid tagger: combination, eg by “vote” • Tags • The labels • Tag Set • The collection of tags used for a particular task, eg Brown or LOB tagset Modified from Diane Litman's version of Steve Bird's notes

  8. Example from the GENIA corpus • Typically a tagged text is a sequence of white-space separated word/tag tokens: These/DT findings/NNS should/MD be/VB useful/JJ for/IN therapeutic/JJ strategies/NNS and/CC the/DT development/NN of/IN immunosuppressants/NNS targeting/VBG the/DT CD28/NN costimulatory/NN pathway/NN ./.

  9. What does Tagging do? • Collapses Distinctions • Lexical identity may be discarded • e.g., all personal pronouns tagged with PRP • Introduces Distinctions • Ambiguities may be resolved • e.g. deal tagged with NN or VB • Helps in classification and prediction Modified from Diane Litman's version of Steve Bird's notes

  10. Significance of Parts of Speech • A word’s POS tells us a lot about the word and its neighbors: • Limits the range of meanings (deal), pronunciation (object vs object) or both (wind) • Helps in stemming • Limits the range of following words • Can help select nouns from a document for summarization • Basis for partial parsing (chunked parsing) • Parsers can build trees directly on the POS tags instead of maintaining a lexicon Modified from Diane Litman's version of Steve Bird's notes

  11. Choosing a tagset • The choice of tagset greatly affects the difficulty of the problem • Need to strike a balance between • Getting better information about context • Make it possible for classifiers to do their job Slide modified from Massimo Poesio's

  12. Some of the best-known Tagsets • Brown corpus: 87 tags • (more when tags are combined, eg isn’t) • LOB corpus: 132 tags • Penn Treebank: 45 tags • Lancaster UCREL C5 (used to tag the BNC): 61 tags • Lancaster C7: 145 tags Slide modified from Massimo Poesio's

  13. The Brown Corpus • An early digital corpus (1961) • Francis and Kucera, Brown University • Contents: 500 texts, each 2000 words long • From American books, newspapers, magazines • Representing genres: • Science fiction, romance fiction, press reportage scientific writing, popular lore Modified from Diane Litman's version of Steve Bird's notes

  14. help(nltk.corpus.brown) • >>> help(nltk.corpus.brown) • | paras(self, fileids=None, categories=None) • | • | raw(self, fileids=None, categories=None) • | • | sents(self, fileids=None, categories=None) • | • | tagged_paras(self, fileids=None, categories=None, simplify_tags=False) • | • | tagged_sents(self, fileids=None, categories=None, simplify_tags=False) • | • | tagged_words(self, fileids=None, categories=None, simplify_tags=False) • | • | words(self, fileids=None, categories=None) • |

  15. nltk.corpus.brown • >>> nltk.corpus.brown.words() • ['The', 'Fulton', 'County', 'Grand', 'Jury', 'said', ...] • >>> nltk.corpus.brown.tagged_words() • [('The', 'AT'), ('Fulton', 'NP-TL'), ...] • >>> nltk.corpus.brown.tagged_sents() • [[('The', 'AT'), ('Fulton', 'NP-TL'), ('County', 'NN-TL'), ('Grand', 'JJ-TL'), ('Jury', 'NN-TL'), ('said', 'VBD'), ('Friday', 'NR'), ('an', 'AT'), ('investigation', 'NN'), …

  16. Penn Treebank • First large syntactically annotated corpus • 1 million words from Wall Street Journal • Part-of-speech tags and syntax trees Modified from Diane Litman's version of Steve Bird's notes

  17. help(nltk.corpus.treebank) • | parsed(*args, **kwargs) • | @deprecated: Use .parsed_sents() instead. • | • | parsed_sents(self, files=None) • | • | raw(self, files=None) • | • | read(*args, **kwargs) • | @deprecated: Use .raw() or .sents() or .tagged_sents() or • | .parsed_sents() instead. • | • | sents(self, files=None) • | • | tagged(*args, **kwargs) • | @deprecated: Use .tagged_sents() instead. • | • | tagged_sents(self, files=None) • | • | tagged_words(self, files=None)

  18. How hard is POS tagging? In the Brown corpus, 12% of word types ambiguous 40% of word tokens ambiguous Slide modified from Massimo Poesio's

  19. Tagging with lexical frequencies • Secretariat/NNP is/VBZ expected/VBN to/TO race/VB tomorrow/NN • People/NNS continue/VBP to/TO inquire/VB the/DT reason/NN for/IN the/DT race/NN for/IN outer/JJ space/NN • Problem: assign a tag to race given its lexical frequency • Solution: we choose the tag that has the greater probability • P(race|VB) • P(race|NN) • Actual estimate from the Switchboard corpus: • P(race|NN) = .00041 • P(race|VB) = .00003 This suggests we should always tag race/NN (correct 41/44=93%) Modified from Massio Poesio's lecture

  20. Reminder • Puns play on our assumptions of the next word… • … eg they present us with an unexpected homonym (bends) • ConditionalFreqDist() counts word-pairs: word bigrams • Used for story generation, Speech recognition, … • Parts of Speech: groups words into grammatical categories • … and separates different functions of a word • In English, many words are ambiguous: 2 or more PoS-tags • Very simple tagger: choose by lexical probability (only) • Better Pos-Taggers: to come…

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