Introduction to Natural Language Processing

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1 Introduction to Natural Language Processing Steven Bird Ewan Klein Edward Loper University of Melbourne, AUSTRALIA University of Edinburgh, UK University of Pennsylvania, USA August 27, 2008

2 Knowledge and Communication in Language human knowledge, human communication, expressed in language language technologies: process human language automatically handheld devices: predictive text, handwriting recognition web search engines: access to information locked up in text two facets of the multilingual information society: natural human-machine interfaces access to stored information

3 Knowledge and Communication in Language human knowledge, human communication, expressed in language language technologies: process human language automatically handheld devices: predictive text, handwriting recognition web search engines: access to information locked up in text two facets of the multilingual information society: natural human-machine interfaces access to stored information

4 Knowledge and Communication in Language human knowledge, human communication, expressed in language language technologies: process human language automatically handheld devices: predictive text, handwriting recognition web search engines: access to information locked up in text two facets of the multilingual information society: natural human-machine interfaces access to stored information

5 Knowledge and Communication in Language human knowledge, human communication, expressed in language language technologies: process human language automatically handheld devices: predictive text, handwriting recognition web search engines: access to information locked up in text two facets of the multilingual information society: natural human-machine interfaces access to stored information

6 Knowledge and Communication in Language human knowledge, human communication, expressed in language language technologies: process human language automatically handheld devices: predictive text, handwriting recognition web search engines: access to information locked up in text two facets of the multilingual information society: natural human-machine interfaces access to stored information

7 Knowledge and Communication in Language human knowledge, human communication, expressed in language language technologies: process human language automatically handheld devices: predictive text, handwriting recognition web search engines: access to information locked up in text two facets of the multilingual information society: natural human-machine interfaces access to stored information

8 Knowledge and Communication in Language human knowledge, human communication, expressed in language language technologies: process human language automatically handheld devices: predictive text, handwriting recognition web search engines: access to information locked up in text two facets of the multilingual information society: natural human-machine interfaces access to stored information

9 Problem awash with language data inadequate tools (will this ever change?) overheads: Perl, Prolog, Java Natural Language Toolkit (NLTK) as a solution

10 Problem awash with language data inadequate tools (will this ever change?) overheads: Perl, Prolog, Java Natural Language Toolkit (NLTK) as a solution

11 Problem awash with language data inadequate tools (will this ever change?) overheads: Perl, Prolog, Java Natural Language Toolkit (NLTK) as a solution

12 Problem awash with language data inadequate tools (will this ever change?) overheads: Perl, Prolog, Java Natural Language Toolkit (NLTK) as a solution

13 NLTK: What you get... Book Documentation FAQ Installation instructions for Python, NLTK, data Distributions: Windows, Mac OSX, Unix, data, documentation CD-ROM: Python, NLTK, documentation, third-party libraries for numerical processing and visualization, instructions Mailing lists: nltk-announce, nltk-devel, nltk-users, nltk-portuguese

14 NLTK: What you get... Book Documentation FAQ Installation instructions for Python, NLTK, data Distributions: Windows, Mac OSX, Unix, data, documentation CD-ROM: Python, NLTK, documentation, third-party libraries for numerical processing and visualization, instructions Mailing lists: nltk-announce, nltk-devel, nltk-users, nltk-portuguese

15 NLTK: What you get... Book Documentation FAQ Installation instructions for Python, NLTK, data Distributions: Windows, Mac OSX, Unix, data, documentation CD-ROM: Python, NLTK, documentation, third-party libraries for numerical processing and visualization, instructions Mailing lists: nltk-announce, nltk-devel, nltk-users, nltk-portuguese

16 NLTK: What you get... Book Documentation FAQ Installation instructions for Python, NLTK, data Distributions: Windows, Mac OSX, Unix, data, documentation CD-ROM: Python, NLTK, documentation, third-party libraries for numerical processing and visualization, instructions Mailing lists: nltk-announce, nltk-devel, nltk-users, nltk-portuguese

17 NLTK: What you get... Book Documentation FAQ Installation instructions for Python, NLTK, data Distributions: Windows, Mac OSX, Unix, data, documentation CD-ROM: Python, NLTK, documentation, third-party libraries for numerical processing and visualization, instructions Mailing lists: nltk-announce, nltk-devel, nltk-users, nltk-portuguese

18 NLTK: What you get... Book Documentation FAQ Installation instructions for Python, NLTK, data Distributions: Windows, Mac OSX, Unix, data, documentation CD-ROM: Python, NLTK, documentation, third-party libraries for numerical processing and visualization, instructions Mailing lists: nltk-announce, nltk-devel, nltk-users, nltk-portuguese

19 NLTK: What you get... Book Documentation FAQ Installation instructions for Python, NLTK, data Distributions: Windows, Mac OSX, Unix, data, documentation CD-ROM: Python, NLTK, documentation, third-party libraries for numerical processing and visualization, instructions Mailing lists: nltk-announce, nltk-devel, nltk-users, nltk-portuguese

20 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

21 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

22 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

23 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

24 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

25 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

26 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

27 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

28 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

29 NLTK: Who it is for... people who want to learn how to: write programs to analyze written language does not presume programming abilities: working examples graded exercises experienced programmers: quickly learn Python (if necessary) Python features for NLP NLP algorithms and data structures

30 NLTK: What you will learn... 1 how to analyze language data 2 key concepts from linguistic description and analysis 3 how linguistic knowledge is used in NLP components 4 data structures and algorithms used in NLP and linguistic data management 5 standard corpora and their use in formal evaluation 6 organization of the field of NLP 7 skills in Python programming for NLP

31 NLTK: What you will learn... 1 how to analyze language data 2 key concepts from linguistic description and analysis 3 how linguistic knowledge is used in NLP components 4 data structures and algorithms used in NLP and linguistic data management 5 standard corpora and their use in formal evaluation 6 organization of the field of NLP 7 skills in Python programming for NLP

32 NLTK: What you will learn... 1 how to analyze language data 2 key concepts from linguistic description and analysis 3 how linguistic knowledge is used in NLP components 4 data structures and algorithms used in NLP and linguistic data management 5 standard corpora and their use in formal evaluation 6 organization of the field of NLP 7 skills in Python programming for NLP

33 NLTK: What you will learn... 1 how to analyze language data 2 key concepts from linguistic description and analysis 3 how linguistic knowledge is used in NLP components 4 data structures and algorithms used in NLP and linguistic data management 5 standard corpora and their use in formal evaluation 6 organization of the field of NLP 7 skills in Python programming for NLP

34 NLTK: What you will learn... 1 how to analyze language data 2 key concepts from linguistic description and analysis 3 how linguistic knowledge is used in NLP components 4 data structures and algorithms used in NLP and linguistic data management 5 standard corpora and their use in formal evaluation 6 organization of the field of NLP 7 skills in Python programming for NLP

35 NLTK: What you will learn... 1 how to analyze language data 2 key concepts from linguistic description and analysis 3 how linguistic knowledge is used in NLP components 4 data structures and algorithms used in NLP and linguistic data management 5 standard corpora and their use in formal evaluation 6 organization of the field of NLP 7 skills in Python programming for NLP

36 NLTK: What you will learn... 1 how to analyze language data 2 key concepts from linguistic description and analysis 3 how linguistic knowledge is used in NLP components 4 data structures and algorithms used in NLP and linguistic data management 5 standard corpora and their use in formal evaluation 6 organization of the field of NLP 7 skills in Python programming for NLP

37 NLTK: Your likely goals... Goals Language Analysis Language Technology Background Arts and Humanities Science and Engineering Programming to manage Language as a source language data, explore linguistic of interesting problems in models, and test data modeling, data min- empirical claims ing, and knowledge discovery Learning to program, with Knowledge of linguistic applications to familiar algorithms and data problems, to work in language structures for high quality, technology or other maintainable language technical field processing software

38 Philosophy practical programming principled pragmatic pleasurable portal

39 Philosophy practical programming principled pragmatic pleasurable portal

40 Philosophy practical programming principled pragmatic pleasurable portal

41 Philosophy practical programming principled pragmatic pleasurable portal

42 Philosophy practical programming principled pragmatic pleasurable portal

43 Philosophy practical programming principled pragmatic pleasurable portal

44 Structure Three parts: 1 Basics: text processing, tokenization, tagging, lexicons, language engineering, text classification 2 Parsing: phrase structure, trees, grammars, chunking, parsing 3 Advanced Topics: selected topics in greater depth: feature-based grammar, unification, semantics, linguistic data management each part: chapter on programming; three chapters on NLP each chapter: motivation, sections, graded exercises, summary, further reading

45 Structure Three parts: 1 Basics: text processing, tokenization, tagging, lexicons, language engineering, text classification 2 Parsing: phrase structure, trees, grammars, chunking, parsing 3 Advanced Topics: selected topics in greater depth: feature-based grammar, unification, semantics, linguistic data management each part: chapter on programming; three chapters on NLP each chapter: motivation, sections, graded exercises, summary, further reading

46 Structure Three parts: 1 Basics: text processing, tokenization, tagging, lexicons, language engineering, text classification 2 Parsing: phrase structure, trees, grammars, chunking, parsing 3 Advanced Topics: selected topics in greater depth: feature-based grammar, unification, semantics, linguistic data management each part: chapter on programming; three chapters on NLP each chapter: motivation, sections, graded exercises, summary, further reading

47 Structure Three parts: 1 Basics: text processing, tokenization, tagging, lexicons, language engineering, text classification 2 Parsing: phrase structure, trees, grammars, chunking, parsing 3 Advanced Topics: selected topics in greater depth: feature-based grammar, unification, semantics, linguistic data management each part: chapter on programming; three chapters on NLP each chapter: motivation, sections, graded exercises, summary, further reading

48 Structure Three parts: 1 Basics: text processing, tokenization, tagging, lexicons, language engineering, text classification 2 Parsing: phrase structure, trees, grammars, chunking, parsing 3 Advanced Topics: selected topics in greater depth: feature-based grammar, unification, semantics, linguistic data management each part: chapter on programming; three chapters on NLP each chapter: motivation, sections, graded exercises, summary, further reading

49 Structure Three parts: 1 Basics: text processing, tokenization, tagging, lexicons, language engineering, text classification 2 Parsing: phrase structure, trees, grammars, chunking, parsing 3 Advanced Topics: selected topics in greater depth: feature-based grammar, unification, semantics, linguistic data management each part: chapter on programming; three chapters on NLP each chapter: motivation, sections, graded exercises, summary, further reading

50 Python: Key Features simple yet powerful, shallow learning curve object-oriented: encapsulation, re-use scripting language, facilitates interactive exploration excellent functionality for processing linguistic data extensive standard library, incl graphics, web, numerical processing downloaded for free from

51 Python: Key Features simple yet powerful, shallow learning curve object-oriented: encapsulation, re-use scripting language, facilitates interactive exploration excellent functionality for processing linguistic data extensive standard library, incl graphics, web, numerical processing downloaded for free from

52 Python: Key Features simple yet powerful, shallow learning curve object-oriented: encapsulation, re-use scripting language, facilitates interactive exploration excellent functionality for processing linguistic data extensive standard library, incl graphics, web, numerical processing downloaded for free from

53 Python: Key Features simple yet powerful, shallow learning curve object-oriented: encapsulation, re-use scripting language, facilitates interactive exploration excellent functionality for processing linguistic data extensive standard library, incl graphics, web, numerical processing downloaded for free from

54 Python: Key Features simple yet powerful, shallow learning curve object-oriented: encapsulation, re-use scripting language, facilitates interactive exploration excellent functionality for processing linguistic data extensive standard library, incl graphics, web, numerical processing downloaded for free from

55 Python: Key Features simple yet powerful, shallow learning curve object-oriented: encapsulation, re-use scripting language, facilitates interactive exploration excellent functionality for processing linguistic data extensive standard library, incl graphics, web, numerical processing downloaded for free from

56 Python Example import sys for line in sys.stdin.readlines(): for word in line.split(): if word.endswith( ing ): print word 1 whitespace: nesting lines of code; scope 2 object-oriented: attributes, methods (e.g. line) 3 readable

57 Comparison with Perl while (<>) { foreach my $word (split) { if ($word =~ /ing$/) { print "$word\n"; } } } 1 syntax is obscure: what are: <> $ my split? 2 it is quite easy in Perl to write programs that simply look like raving gibberish, even to experienced Perl programmers (Hammond Perl Programming for Linguists 2003:47) 3 large programs difficult to maintain, reuse

58 What NLTK adds to Python NLTK defines a basic infrastructure that can be used to build NLP programs in Python. It provides: Basic classes for representing data relevant to natural language processing Standard interfaces for performing tasks, such as tokenization, tagging, and parsing Standard implementations for each task, which can be combined to solve complex problems Demonstrations (parsers, chunkers, chatbots) Extensive documentation, including tutorials and reference documentation

59 What NLTK adds to Python NLTK defines a basic infrastructure that can be used to build NLP programs in Python. It provides: Basic classes for representing data relevant to natural language processing Standard interfaces for performing tasks, such as tokenization, tagging, and parsing Standard implementations for each task, which can be combined to solve complex problems Demonstrations (parsers, chunkers, chatbots) Extensive documentation, including tutorials and reference documentation

60 What NLTK adds to Python NLTK defines a basic infrastructure that can be used to build NLP programs in Python. It provides: Basic classes for representing data relevant to natural language processing Standard interfaces for performing tasks, such as tokenization, tagging, and parsing Standard implementations for each task, which can be combined to solve complex problems Demonstrations (parsers, chunkers, chatbots) Extensive documentation, including tutorials and reference documentation

61 What NLTK adds to Python NLTK defines a basic infrastructure that can be used to build NLP programs in Python. It provides: Basic classes for representing data relevant to natural language processing Standard interfaces for performing tasks, such as tokenization, tagging, and parsing Standard implementations for each task, which can be combined to solve complex problems Demonstrations (parsers, chunkers, chatbots) Extensive documentation, including tutorials and reference documentation

62 What NLTK adds to Python NLTK defines a basic infrastructure that can be used to build NLP programs in Python. It provides: Basic classes for representing data relevant to natural language processing Standard interfaces for performing tasks, such as tokenization, tagging, and parsing Standard implementations for each task, which can be combined to solve complex problems Demonstrations (parsers, chunkers, chatbots) Extensive documentation, including tutorials and reference documentation

63 NLTK Design: Requirements 1 simplicity: intuitive framework with substantial building blocks 2 consistency: uniform data structures, interfaces predictability 3 extensibility: accommodates new components (replicate vs extend exiting functionality) 4 modularity: interaction between components 5 well-documented: substantial documentation

64 NLTK Design: Requirements 1 simplicity: intuitive framework with substantial building blocks 2 consistency: uniform data structures, interfaces predictability 3 extensibility: accommodates new components (replicate vs extend exiting functionality) 4 modularity: interaction between components 5 well-documented: substantial documentation

65 NLTK Design: Requirements 1 simplicity: intuitive framework with substantial building blocks 2 consistency: uniform data structures, interfaces predictability 3 extensibility: accommodates new components (replicate vs extend exiting functionality) 4 modularity: interaction between components 5 well-documented: substantial documentation

66 NLTK Design: Requirements 1 simplicity: intuitive framework with substantial building blocks 2 consistency: uniform data structures, interfaces predictability 3 extensibility: accommodates new components (replicate vs extend exiting functionality) 4 modularity: interaction between components 5 well-documented: substantial documentation

67 NLTK Design: Requirements 1 simplicity: intuitive framework with substantial building blocks 2 consistency: uniform data structures, interfaces predictability 3 extensibility: accommodates new components (replicate vs extend exiting functionality) 4 modularity: interaction between components 5 well-documented: substantial documentation

68 NLTK Design: Non-requirements 1 encyclopedic: has many gaps; opportunity for students to extend it 2 efficiency: not highly optimised for runtime performance 3 programming tricks: avoid in preference for clear implementations (replicate vs extend exiting functionality)

69 NLTK Design: Non-requirements 1 encyclopedic: has many gaps; opportunity for students to extend it 2 efficiency: not highly optimised for runtime performance 3 programming tricks: avoid in preference for clear implementations (replicate vs extend exiting functionality)

70 NLTK Design: Non-requirements 1 encyclopedic: has many gaps; opportunity for students to extend it 2 efficiency: not highly optimised for runtime performance 3 programming tricks: avoid in preference for clear implementations (replicate vs extend exiting functionality)

71 Corpora Distributed with NLTK Australian ABC News, 2 genres, 660k words, sentence-segmented Brown Corpus, 15 genres, 1.15M words, tagged CMU Pronouncing Dictionary, 127k entries CoNLL 2000 Chunking Data, 270k words, tagged and chunked CoNLL 2002 Named Entity, 700k words, pos- and named-entity-tagged (Dutch, Spanish) Floresta Treebank, 9k sentences (Portuguese) Genesis Corpus, 6 texts, 200k words, 6 languages Gutenberg (sel), 14 texts, 1.7M words Indian POS-Tagged Corpus, 60k words pos-tagged (Bangla, Hindi, Marathi, Telugu) NIST 1999 Info Extr (sel), 63k words, newswire and named-entity SGML markup Names Corpus, 8k male and female names PP Attachment Corpus, 28k prepositional phrases, tagged as noun or verb modifiers Presidential Addresses, 485k words, formatted text Roget s Thesaurus, 200k words, formatted text SEMCOR, 880k words, part-of-speech and sense tagged SENSEVAL 2, 600k words, part-of-speech and sense tagged Shakespeare XML Corpus (sel), 8 books Stopwords Corpus, 2,400 stopwords for 11 languages Switchboard Corpus (sel), 36 phonecalls, transcribed, parsed Univ Decl Human Rights, 480k words, 300+ languages US Pres Addr Corpus, 480k words Penn Treebank (sel), 40k words, tagged and parsed TIMIT Corpus (sel), audio files and transcripts for 16 speakers Wordlist Corpus, 960k words and 20k affixes for 8 languages WordNet, 145k synonym sets

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