Statistical NLP: Lecture 6

1. Statistical NLP: Lecture 6 Corpus-Based Work (Ch 4)

2. Corpus-Based Work • Text Corpora are usually big. • Corpora 사용의 중요한 한계점으로 작용 • 대용량 Computer의 발전으로 극복 • Corpus-Based word involves collection a large number of counts from corpora that need to be access quickly • There exists some software for processing corpora

3. Corpora • Linguistically mark-up or not • Representative sample of the population of interest • American English vs. British English • Written vs. Spoken • Areas • The performance of a system depends heavily on • the entropy • Text categorization • Balanced corpus vs. all text available

4. Software • Software • Text editor : 글자그대로 보여준다. • Regular expression : 정확한 patter을 찾게 한다. • Programming language • C/C++, Perl, awk, Python, Prolog, Java • Programming techniques

5. Looking at Text • Textcome a row format or marked up. • Markup • A term is used for putting code of some sort into a computer file • Commercial word processing : WYSIWYG • Features of text in human languages • 자연어 처리의 어려운 점

6. Low-Level Formatting Issues • Junk formatting/Content. • document headers and separators, typesetter codes, table and diagrams, garbled data in the computer file. • OCR : If your program is meant to deal with only connected English text • Uppercase and Lowercase: • should we keep the case or not? The, the and THE should all be treated the same but “brown” in “George Brown” and “brown dog” should be treated separately.

7. Tokenization: What is a Word?(1) • Tokenization • To divide the input text into unit called token • what is a word? • graphic word (Kucera and Francis. 1967) “a string of contiguous alphanumeric characters with space on either side;may include hyphens and apo-strophes, but no other punctuation marks”

8. Tokenization: What is a Word?(2) • Period • 문자의 끝을 나타내는 의미가 있다. • 약어를 나타낸다. : as in etc. or Wash • Single apostrophes • isn’t, I’ll  2 words ? 1 words • 영어의 축약 : I’ll or isn’t • Hyphenation • 일반적으로 인쇄상 다음 줄로 넘어가는 한 단어를 표시 • text-based, co-operation, e-mail, A-1-plus paper, “take-it-or-leave-it”, the 90-cent-an-hour raise, mark up  mark-up  mark(ed) up

9. Tokenization: What is a Word?(3) • Word Segmentation in other languages: no whitespace ==> words segmentation is hard • whitespace not indicating a word break. • New York, data base • the New York-New Haven railroad • 명확한 의미의 정보가 다양한 형태로 존재한다. • +45 43 48 60 60, (202) 522-2230, 33 1 34 43 32 26, (44.171) 830 1007

10. Tokenization: What is a Word?(4) Phone number Country Phone number Country 0171 378 0647 UK +45 43 60 60 Denmark (44.171) 830 1007 UK 95-51-279648 Pakistan +44 (0) 1225 753678 UK +411/284 3797 Switzerland 01256 468551 UK (94-1) 866854 Sri Lanka (202) 522-2330 USA +49 69 136-2 98 05 Germany 1-925-225-3000 USA 33 1 34 43 32 26 France 212.995.5402 USA ++31-20-5200161 The Nerherlands Table 4.2 Different formats for telephone numbers appearing in an issue of the Economist

11. Morphology • Stemming: Strips off affixes. • sit, sits, sat • Lemmatization: transforms into base form (lemma, lexeme) • Disambiguation • Not always helpful in English (from an IR point of view) which has very little morphology. • IR community has shown that doing stemming does not help the performance • Mutiple words  a morpheme ??? • Morphological analysis를 구현하기 위한 추가비용에 비해 효능이 안 좋다

12. Stemming • 동일 의 단어의 다양한 변형을 하나의 색인어로 변환 • “computer”, “computing” 등을 “compute”로 변환 • 장점 • 저장 공간의 사용을 감소, 검색 속도 개선 • 검색 결과의 질 향상(질의가 “compute”일 경우 “computer”, “computing”등 포함 하는 모든 단어 검색) • 단점 • Over Stemming: 문자를 과도하게 제거하여 연관성 없는 단어들의 매칭을 발생 • Under Stemming : 단어에 포함된 문자를 적게 제거하여 연관성 있는 단어 매칭이 안 되는 현상

13. Porter Stemming Algorithm • 가장 널리 사용되며, 다양한 규칙을 이용 • 접두사는 제거하지 않고 접미사만을 제거하거나, 새로운 String으로 대치 • Porter Stemming 실행 전 • Porter Stemming 실행 후

14. Porter Stemming Algorithm

15. Porter Stemming Algorithm • Error #1: Words ending with “yed” and “ying” and having different meanings may end up with • Dying -> dy (impregnate with dye) • Dyed -> dy (passes away) • Error #2: The removal of “ic” or “ical” from words having m=2 and ending with a series of consonant, vowel, consonant, vowel, such as generic, politic…: • Political -> polit • Politic -> polit • Polite -> polit

16. Sentences • What is a sentence? • Something ending with a ‘.’, ‘?’ or ‘!’. True in 90% of the cases. • Colon, semicolon, dash도 문장으로 여겨질 수 있다. • Sometimes, however, sentences are split up by other punctuation marks or quotes. • Often, solutions involve heuristic methods. However, these solutions are hand-coded. Some effort to automate the sentenceboundary process have also been done. • 우리말은 더욱 어려움!!! • 마침표가 없기도 하고  종결형 어미 뒤? • 연결형 어미이면서 종결형 어미 • 따옴표

17. End-of-Sentence Detection (I) • Place EOS after all . ? ! (maybe ;:-) • Move EOS after quotation marks, if any • Disqualify a period boundary if: – Preceeded by known abbreviation followed by upper case letter, not normally sentence-final: e.g., Prof. vs. Mr.

18. End-of-Sentence Detection (II) – Precedeed by a known abbreviation not followed by upper case: e.g., Jr. etc. (abbreviation that is sentence-final or medial) • Disqualify a sentence boundary with ? or ! If followed by a lower case (or a known name) • Keep all the rest as EOS

19. Marked-Up Data I: Mark-up Schemes • 초기의 markup schemes • 단순히 내용정보만을 위해 header에 삽입(giving author, date, title, etc.) • SGML • 문서의 구조와 문법을 표준화하는 grammer language • XML • SGML을 web에 응용하기 위해 만든 SGML의 축소판

20. Marked-Up Data II: Grammaticaltagging • first step of analysis • 일반적인 문법적 category로 구별하는 것 • 최상급, 비교급, 명사의 단수, 복수 등의 구별 • Tag sets (Table 4.5) • morphological distinction 을 통합한다. • The design of a tag set • 분류의 관점 • Word의 문법정보가 얼마나 유용한 요소인가 하는 관점 • 예상의 관점 • 문맥에서 다른 word에 어떠한 영향을 미치는지 예상하는 관점

21. Examples of Tagset(Korean)

22. Examples of Tagset(English) PennTreebank tagset Brown corpus tagset