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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