What is, discuss, true or false, choose Text segmentation

Word segmentation Sentence splitting Word segmentation Segmentation problems Sentence segmentation Automatic segmentation approaches , part-of-speech tagging (POS tagging or POST),grammatical tagging, word-category disambiguation POS Principle Use of Hidden Markov Models HMMs CLAWS Dynamic Programming methods Unsupervised taggers Other taggers and methods, issues Parsing Traditional methods vs. computational methods Parser, types
Top-down parsers
Some of the parsers that use top-down parsing include:
• Recursive descent parser
• LL parser (Left-to-right, Leftmost derivation) • Earley parser
Bottom-up parsers
Some of the parsers that use bottom-up parsing include:
• Precedence parser
• Operator-precedence parser
• Simple precedence parser
• BC (bounded context) parsing
• LR parser (Left-to-right, Rightmost derivation)

Parser development software
Some of the well known parser development tools include the following. Also see comparison of parser generators.
• ANTLR
• Bison
• Coco/R
• GOLD
• JavaCC

Lookahead Finite-state machine: first 4 paragraphs Concepts and vocabulary Transducers Discourse analysis Topics of interest Perspectives Dialogical analysis definition

Review Questions:
1- What is bayes rule?
2- Why studying Bayes Rule is important?
3- What is the advantage of the Baysian Approach? Slide 18
4- What is it used for? Slide 18
5- What are the elements of the Baysian Reasoning? Slide 19
6- Mention the bayes rule formula?
7- A problem:
Sue has written 100 essays of 5000 words in 3 years. She made spelling mistakes for the word determine which was misspelled for ditermine 100 times. Tomorrow, she has a TOEFL exam. What are the chances that she would make mistakes for the word determine tomorrow considering the fact that in the first 2 years she made 90 mistakes and in the third year she corrected her mistakes which became 10 only? In order to answer this please review this website: http://stattrek.com/probability/bayes-theorem.aspx
- First you have to mention the formula which is half of the grade.
8- MLE
8- Definition
9- Purpose
10- Formula
11- A question: Divide the number of occurences of a single word on the total number of a corpus.Example, The word THE Occured 100 times in a corpus of 10000 words so the MLE for THEis: 100/10000= 0.01
Bayes rule ppt p 5, 8, 18, 19

Todays Class 18th Feb, 2013 on MLE: Maximum Likelihood estimation
Divide the number of occurences of a single word on the total number of a corpus.
Example, The word THE Occured 100 times in a corpus of 10000 words so the MLE for THEis: 100/10000= 0.01

What is, discuss, true or false, choose

Text segmentation

Word segmentation

Sentence splitting

Word segmentation

Segmentation problems

Sentence segmentation

Automatic segmentation approaches

, part-of-speech tagging (POS tagging or POST),grammatical tagging, word-category disambiguation

POS Principle

Use of Hidden Markov Models HMMs

CLAWS

Dynamic Programming methods

Unsupervised taggers

Other taggers and methods, issues

Parsing

Traditional methods vs. computational methods

Parser, types

Top-down parsers

Some of the parsers that use top-down parsing include:

• Recursive descent parser

• LL parser (Left-to-right, Leftmost derivation) • Earley parser

Bottom-up parsers

Some of the parsers that use bottom-up parsing include:

• Precedence parser

• Operator-precedence parser

• Simple precedence parser

• BC (bounded context) parsing

• LR parser (Left-to-right, Rightmost derivation)

Parser development software

Some of the well known parser development tools include the following. Also see comparison of parser generators.

• ANTLR

• Bison

• Coco/R

• GOLD

• JavaCC

Lookahead

Finite-state machine: first 4 paragraphs

Concepts and vocabulary

Transducers

Discourse analysis

Topics of interest

Perspectives

Dialogical analysis definition

major: 25%

summary sheets: 20

summary presentation: 15

_

60

1- What is bayes rule?

2- Why studying Bayes Rule is important?

3- What is the advantage of the Baysian Approach? Slide 18

4- What is it used for? Slide 18

5- What are the elements of the Baysian Reasoning? Slide 19

6- Mention the bayes rule formula?

7- A problem:

Sue has written 100 essays of 5000 words in 3 years. She made spelling mistakes for the word

determinewhich was misspelled forditermine100 times. Tomorrow, she has a TOEFL exam. What are the chances that she would make mistakes for the worddeterminetomorrow considering the fact that in the first 2 years she made 90 mistakes and in the third year she corrected her mistakes which became 10 only?In order to answer this please review this website: http://stattrek.com/probability/bayes-theorem.aspx- First you have to mention the formula which is half of the grade.

8- MLE

8- Definition

9- Purpose

10- Formula

11- A question: Divide the number of occurences of a single word on the total number of a corpus.Example, The word

THEOccured 100 times in a corpus of 10000 words so the MLE foris: 100/10000= 0.01THEBayes rule ppt p 5, 8, 18, 19

OUR GENERATED BOOK FOR THE COURSE

http://nlp.stanford.edu/links/statnlp.html#Taggers

Todays Class 18th Feb, 2013 on MLE: Maximum Likelihood estimationDivide the number of occurences of a single word on the total number of a corpus.

Example, The word

THEOccured 100 times in a corpus of 10000 words so the MLE foris: 100/10000= 0.01THE