Nltk natural language toolkit overview and application @ PyCon.tw 2012
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This document provides an overview of the Natural Language Toolkit (NLTK) and its applications in natural language processing (NLP). It discusses the features of NLTK, including text tokenization, normalization, analysis, part-of-speech tagging, text classification, and named entity recognition. Additionally, it highlights an application called 聚寶評, a food search engine that utilizes sentiment analysis and offers both web and mobile platforms.
![Annotated Text Corpora (2/3)
nltk.corpus
#Chinese treebank
import nltk nltk.corpus.sinica_treebank
#download corpus on demand
nltk.download() #Examples
(嘉珍, Nba)
#stopwords (和, Caa)
nltk.corpus.stopwords.words() (我, Nhaa)
nltk.corpus.stopwords.words('english') (住在, VC1)
nltk.corpus.stopwords.words('french') (同一條, DM)
(巷子, Nab)
some_english_stopwords = ['most', 'me',
'below', 'when', 'which', 'what', 'of', 'it', (我們, Nhaa)
'very', 'our'] (是, V_11)
(鄰居, Nab)
(也, Dbb)
(是, V_11)
(同班, Nv3)
(同學, Nab) 12](https://image.slidesharecdn.com/nltk-naturallanguagetoolkitoverviewandapplication-pycon-120610071219-phpapp01/85/Nltk-natural-language-toolkit-overview-and-application-PyCon-tw-2012-12-320.jpg)
![Text Tokenization
nltk.tokenize
Web Text Processing Flow
HTML ASCII Text Vocabulary
from urllib import urlopen
html = urlopen(url).read()
raw = nltk.clean_html(html)
sents = nltk.sent_tokenize(raw)
tokens = [] # wordpunct_tokenize: ['3', '.', '33']
for sent in sents: # word_tokenize: ['3.33']
tokens.extend(nltk.word_tokenize(sent))
text = nltk.Text(tokens)
words = [word.lower() for word in text]
vocab = sorted(set(words))
14](https://image.slidesharecdn.com/nltk-naturallanguagetoolkitoverviewandapplication-pycon-120610071219-phpapp01/85/Nltk-natural-language-toolkit-overview-and-application-PyCon-tw-2012-14-320.jpg)
Nltk natural language toolkit overview and application @ PyCon.tw 2012
- 1. NLTK: Natural Language Toolkit Overview and Application Jimmy Lai [email protected] Software Engineer @ 引京聚點 2012/06/09 1
- 2. Outline 1. An application based on NLP: 聚寶評 2. Introduction to Natural Language Processing 3. Brief History of NLTK 4. NLTK 2
- 3. 聚寶評 www.ezpao.com 美食搜尋引擎 搜尋各大部落格食記 3
- 4. 聚寶評 www.ezpao.com 語意分析搜尋引擎 4
- 6. 手機版 網友分享菜+評論分析 m.ezpao.com 網友分享菜+正負評分析 6
- 7. Natural Language Processing (NLP) • 語音識別（Speech recognition） • 詞性標註（Part-of-speech tagging） • 句法分析（Parsing） • 自然語言生成(Natural language generation) • 文本分類（Text classification） • 信息抽取（Information extraction） • 機器翻譯（Machine translation） • 文字蘊涵（Textual entailment） via Wikipedia 7
- 8. NLTK: Natural Language Toolkit • http://www.nltk.org/ • Author: Steven Bird, Edward Loper, Ewan Klein • Originally developed for class student has background either in computer science or linguistics. • Currently: – Education: over 100 courses in 23 countries. – Research: over 250 papers cites NLTK. 8
- 9. Outline 1. An application based on NLP: 聚寶評 2. Introduction to Natural Language Processing 3. Brief History of NLTK 4. NLTK: a. Installation b. Annotated Text Corpora c. Text Tokenization, Normalization, Analysis, Distribution Analysis d. Part-of-speech Tagging e. Text Classification f. Named Entity Recognition 9
- 10. Install NLTK Python 2.6+ pip install numpy pip install matplotlib pip install nltk 10
- 11. Annotated Text Corpora (1/3) nltk.corpus • Corpus: 語料庫，含有某種結構化標記的資 料集合，可能包含多種語言。 • 例如： – stopwords: 常見字字典 – sinica_treebank: 中文語句結構標記語料庫 – brown: 包含15種分類及詞性標記的英語語料庫 – wordnet: 包含詞性、同義反義的英語字典 11
- 12. Annotated Text Corpora (2/3) nltk.corpus #Chinese treebank import nltk nltk.corpus.sinica_treebank #download corpus on demand nltk.download() #Examples (嘉珍, Nba) #stopwords (和, Caa) nltk.corpus.stopwords.words() (我, Nhaa) nltk.corpus.stopwords.words('english') (住在, VC1) nltk.corpus.stopwords.words('french') (同一條, DM) (巷子, Nab) some_english_stopwords = ['most', 'me', 'below', 'when', 'which', 'what', 'of', 'it', (我們, Nhaa) 'very', 'our'] (是, V_11) (鄰居, Nab) (也, Dbb) (是, V_11) (同班, Nv3) (同學, Nab) 12
- 13. Annotated Text Corpora (3/3) nltk.corpus ACE Named Entity Chunker (Maximum entropy) Portuguese Treebank Australian Broadcasting Commission 2006 Gazeteer Lists Genesis Corpus Alpino Dutch Treebank Project Gutenberg Selections BioCreAtIvE (Critical Assessment of Information NIST IE-ER DATA SAMPLE Extraction Systems in Biology) C-Span Inaugural Address Corpus Brown Corpus Indian Language POS-Tagged Corpus Brown Corpus (TEI XML Version) JEITA Public Morphologically Tagged Corpus (in CESS-CAT Treebank ChaSen format) PC-KIMMO Data Files CESS-ESP Treebank KNB Corpus (Annotated blog corpus) Chat-80 Data Files Language Id Corpus City Database Lin's Dependency Thesaurus The Carnegie Mellon Pronouncing Dictionary (0.6) MAC-MORPHO: Brazilian Portuguese news text with ComTrans Corpus Sample part-of-speech tags Machado de Assis -- Obra Completa CONLL 2000 Chunking Corpus Sentiment Polarity Dataset Version 2.0 CONLL 2002 Named Entity Recognition Corpus Names Corpus, Version 1.3 (1994-03-29) Dependency Treebanks from CoNLL 2007 (Catalan NomBank Corpus 1.0 and Basque Subset) NPS Chat Dependency Parsed Treebank Paradigm Corpus Sample European Parliament Proceedings Parallel Corpus 13
- 14. Text Tokenization nltk.tokenize Web Text Processing Flow HTML ASCII Text Vocabulary from urllib import urlopen html = urlopen(url).read() raw = nltk.clean_html(html) sents = nltk.sent_tokenize(raw) tokens = [] # wordpunct_tokenize: ['3', '.', '33'] for sent in sents: # word_tokenize: ['3.33'] tokens.extend(nltk.word_tokenize(sent)) text = nltk.Text(tokens) words = [word.lower() for word in text] vocab = sorted(set(words)) 14
- 15. Text Normalization (1/2) nltk.stem • Stem: 將單字(現在式、過去式、單複數)還 原成原型。可以將不同形式的單字歸類為 同一個單字。 • 著名演算法：Porter Stemmer 15
- 16. Text Normalization (2/2) nltk.stem 16
- 17. Text Analysis nltk.text 17
- 18. Text Distribution Analysis nltk.probability 18
- 19. Part-of-speech Tagging (1/3) nltk.tag • Part of Speech Tagging: 詞性標記， 標記每個單字的詞性。 • 同一單字的不同詞性其語義不同， 如Book名詞是書，動詞是預定。 • 透過POS Tagging，可以賦予文字更 多語義資訊。 19
- 20. Part-of-speech Tagging (2/3) nltk.tag 20
- 21. Part-of-speech Tagging (3/3) nltk.tag 21
- 22. Text Classification (1/3) nltk.classify • Text Classification: 文字分類，分析文字後將 文字分到預先定義的類別裡。 • 基於統計的機器學習演算法，著名的演算 法為： – Naïve Bayes Classifier – Decision Tree – Support Vector Machine 22
- 23. Text Classification (2/3) nltk.classify Machine Learning Approach Work Flow Training Text with Feature Learning Classifier Features Label Generation Algorithms Model Prediction Text Classifier Feature without Features Label Generation Model Label 23
- 24. Text Classification (3/3) nltk.classify 24
- 25. Named Entity Recognition (NER) (1/2) nltk.tag, nltk.chunk • Named Entity Recognition: 從文字中擷取出 命名實體，命名實體是具有完整語義的複 合單字。例如：人名、地名、事件。 NER General Work Flow Sentence Named Entity Relation Tokenization POS tagging Segmentation Recognition Recognition 25
- 26. Named Entity Recognition (NER) (2/2) nltk.tag, nltk.chunk 26
- 27. Reference 1. Steven Bird, Ewan Klein, and Edward Loper, “Natural Language Processing with Python”, 2009. #includes: Python + NLP + NLTK 2. Jacob Perkins, “Python Text Processing with NLTK 2.0 Cookbook”, 2010. 3. Matthew A. Russell, “Mining the Social Web”, 2011. 4. Loper, E., & Bird, S. (2002). NLTK: The Natural Language Toolkit. Proceedings of the ACL02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics. 27
- 28. Thank you for your attention. Q&A We are hiring! • 核心引擎演算法研發工程師 • 系統研發工程師 • 網路應用研發工程師 Oxygen Intelligence Taiwan Limited 引京聚點 知識結構搜索股份有限公司 • 公司簡介： http://www.ezpao.com/about/ • 職缺簡介： http://www.ezpao.com/join/ • 請將履歷寄到 [email protected] 28