CS计算机代考程序代写 information retrieval deep learning information theory python algorithm Excel COMP5046

COMP5046
Natural Language Processing
Lecture 1: Course Info & Introduction to NLP
Dr. Caren Han
Semester 1, 2021
School of Computer Science, University of Sydney

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LECTURE PLAN
Lecture 1: Introduction to Natural Language Processing
1. Course Introduction
2. Overview of Natural Language Processing (NLP)
3. Word Meaning and Representation
4. Count-based Word Representation
• One-hot Encoding
• Bag of Words
• Term Frequency-Inverse Document Frequency
5. Next Week Preview
• Prediction-based Word Representation

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INTRODUCTION Dr Caren Han
Education
• B CompScience (1st Class Honours)
• PhD Computer Science (Artificial Intelligence)
Australian Young Achiever Teaching Excellence Award Teacher of the Year 2020 Award
• Machine Learning, Natural Language Processing, etc.
NLP Research Experience (8 years experience)
• U.S. Air Force: Robotics Natural Language Processing Interface
• U.S. Navy: Deep Learning with NLP
• Hyundai Co.: Car Navigation Dialog System
• NLP Top-tier Conferences Best Paper Award/Best Area Paper Award

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INTRODUCTION
COMP5046 Natural Language Processing
This unit introduces computational linguistics and the statistical techniques and algorithms used to automatically process natural languages. It will review the core statistics and information theory, and the basic linguistics, required to understand natural language processing (NLP).
NLP is used in a wide range of applications, including information retrieval and extraction; question answering; machine translation; and classifying and clustering of documents. This unit will explore the key challenges of natural language to computational modelling, and the state-of-the-art approaches to the key NLP sub-tasks, including tokenisation, morphological analysis, word sense representation, part-of-speech tagging, named entity recognition and other information extraction.
Students will implement many of these sub-tasks in labs and assignments, that can be used in the real-world cases. The unit will also investigate the annotation process that is central to creating training data for interesting application. With this unit, students can develop the innovative application that can be used in the real world.
Where to find the course information?
Unit Outline – COMP5046
https://www.sydney.edu.au/units/COMP5046
Canvas – COMP5046
https://canvas.sydney.edu.au/courses/30912

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INTRODUCTION
What will you learn in this course?
The focus of this course is on the review and comparison of models and methods that have achieved state-of-the-art results on various NLP tasks such as question answering (QA) and machine translation.
In this comprehensive review, students will get a detailed understanding of the past, present, and future of NLP. In addition, students will learn some of the current best practices for applying deep learning in NLP
word2vec
NN
Language Modelling
Dependency Parsing
Part-of-Speech Tagging
Transformer

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INTRODUCTION
What will you learn in this course?
Week 1: Introduction to Natural Language Processing (NLP)
Week 2: Word Embeddings (Word Vector for Meaning)
Week 3: Word Classification with Machine Learning I
Week 4: Word Classification with Machine Learning II
NLP and Machine Learning
Week 5: Language Fundamental
Week 6: Part of Speech Tagging
Week 7: Dependency Parsing
Week 8: Language Model and Natural Language Generation
NLP Techniques
Week 9: Information Extraction: Named Entity Recognition
Advanced Topic
Week 10: Advanced NLP: Attention and Reading Comprehension
Week 11: Advanced NLP: Transformer and Machine Translation
Week 12: Advanced NLP: Pretrained Model in NLP
Week 13: Future of NLP and Exam Review

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EXPECTATIONS
I DO assume you can program
• By that, I mean you are a confident programmer
• Labs will involve programming
• Assessment will involve programming
• Python recommended; other popular languages accepted
• There will be NO NON-programming option for assignments
• But it’s more than just programming:
▪ algorithms, mathematics and (esp.) statistics ▪ linguistics and intuition about language
▪ analytical thinking

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EXPECTATIONS
I DO NOT assume you are a linguist
• But you do need to know roughly how to identify a noun/verb/etc.
• We will think critically about how we use language
• and about how computational models capture aspects of language
Abc

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EXPECTATIONS
I DO NOT assume you are a deep learning researcher
• But you do need to know (really) roughly how machine learning works.
• We will think critically how to use text data and embeddings
• and about how deep learning models capture aspects of language (context)
Abc

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The NLP Big Picture
The purpose of Natural Language Processing: Overview
Understanding
Searching
Generation
Translation
Dialog
Sentiment Analysis
Topic Classification
Topic Modelling
….
Search
….
Entity Extraction
When Sebastian Thrun …
Claudia sat on a stool
She sells seashells
Drinking, Drank, Drunk How is the weather today
[she/PRP] [sells/VBZ] [seashells/NNS]
Drink
[How] [is] [the] [weather] [today]
Parsing
PoS Tagging
Stemming
Tokenisation
NLP Stack Application

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The NLP ERA
Hype Cycle for Artificial Intelligence
(Gartner, 2020)

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The NLP ERA
NLP Techniques – with the Trend of Machine Translation

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The NLP ERA
Language Modelling using Deep Learning-based NLP Techniques

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ASSESSMENT
Assessment Overview
Assessment
Weight
Due
Lab Exercise
10%
Multiple Weeks
Assignment 1
20%
Week 8 (Friday 11.59pm – AU time)
Assignment 2
20%
Week 14 (Friday 11.59pm – AU time)
Final Exam
50%
Exam Period
Lab Exercises
• Programming tasks done in weekly computer labs
Assignments
• Take place through the teaching period
• Implementation and Documentation Penalties for lateness
• 10% of the available marks per day late; maximum 7 days late

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ASSESSMENT Lab Exercise – 10%
• • •
In the Lab, students need to do the small tasks (1% for each week). 2-3 tasks are given based on what you learned in the previous lecture.
You must have been assessed as having completed 10 out of 11 in order to get the 10% for weekly lab exercise.
word2vec
NN
RL
Dependency Parsing Part-of-Speech Tagging Language Generation

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ASSESSMENT
Lab Exercise – 10%
• When to submit the Weekly Lab Exercise (e.g. Lab1 Release and Submission)
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Week 1
Lecture1
Lab1 Release
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Week 2
Lecture2 Lab1
Lab2 Release
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Week 3
Lab 1 Due
Lecture3 Lab2

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ASSESSMENT
What do we do during Labs?
In Labs, Students will use Google CoLab
Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.
https://colab.research.google.com/notebooks/intro.ipynb

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ASSESSMENT Assignment 1 (20%)
The focus of this assignment 1 is implementing the text/word embeddings/language models, which 1)understands the language and 2)produces the detection/prediction/generation decision.
NOTE: Assignment 1 will be an individual assignment. Assignment 2 (20%)
The focus of this assignment 2 is proposing a natural language model to produce high performance (such as accuracy, consistency, plausibility, validity, and distribution) in different NLP tasks. The different NLP tasks can be Question Answering, Named Entity Recognition, Text Classification, or etc.
NOTE: Assignment 2 would be a group assignment (2 people in group). However, you can do individually only if you want.
Final Exam (50%)
The final exam will be a short take-home exam hosted on Canvas (3 hours duration). You will be asked to answer variety of theoretical questions.
The sample exam questions will be shared in the week 13 lecture.

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ASSESSMENT ASSESSMENT Due
Week 3: Word Classification with Machine Learning I
Week 1: Introduction to Natural Language Processing (NLP)
Week 2: Word Embeddings (Word Vector for Meaning)
Week 4: Word Classification with Machine Learning II
Week 5: Language Fundamental
Week 6: Part of Speech Tagging
Week 7: Dependency Parsing
Week 8: Language Model and Natural Language Generation
Assignment 1 Due
Week 9: Information Extraction: Named Entity Recognition
Week 10: Advanced NLP: Attention and Reading Comprehension
Week 11: Advanced NLP: Transformer and Machine Translation
Week 12: Advanced NLP: Pretrained Model in NLP
Week 13: Future of NLP and Exam Review
Assignment 2 Due (Week 14)

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ASSESSMENT
Start assignments early!
• All assignments involve coding and report writing
• Reports are the primary deliverable
• Though we will check implementations for correctness
• Assignments will Be very different from last year’s
• Reports will be submitted to Turnitin through Canvas
• Code is also submitted (for assignments 1 and 2) and retained
• We will use code plagiarism detection tools
• Clearly reference any copied/adapted code portions and cite their origins

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ASSESSMENT
Start assignments early!
• Starting early means you will work while you sleep
• Don’t waste all your time on code
• The report is more important, but largely depends on the code
• If you’re stuck, ask early
• We might be able to offer you alternatives

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TIMETABLE Working Hours
• Work 12 hours per week for this course (including 3 contact hours);
• Attend 2 hours of lectures per week: ▪ Tuesday 3 – 5pm
▪ Lectures are recorded, but don’t depend on it!
• Attend 1 hour of tutorial/laboratory time
• Participate respectfully in discussions in lectures and labs;
• Complete all assessment tasks on time.

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TIMETABLE
Classes and Release Date
• When the Class and Release Dates are
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Week 1
Lecture1
Lab1 Release
Lecture2 Release
Lab2 Release
Lecture3 Release
Lab3 Release
Lecture4 Release
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Week 2
Lecture2 Lab1
Live QA
Mon
Tue
Wed
Thu
Fri
Sat
Sun
Week 3
Lecture3 Lab2
Live QA

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TIMETABLE
Full Course Timetable at course website:
• Lecture: Tue 3-5pm
• Tutorial: Tue 5-6pm, 6-7pm / Wed 5-6pm, 6-7pm
• LiveQA: Thu, Fri, Sat 9-10pm

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STAFF Who are we?
Unit Coordinator & Lecturer
• Caren.Han@sydney.edu.au
• No fixed consultation hour; please arrange a time to see me.
Teaching Assistants & Tutors
• They will introduce themselves really soon!
For Qs related to course content, please post in Ed.
For Qs related to admin, please contact to the unit coordinator • Please put [COMP5046] in the title of the email

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READINGS (OPTIONAL)
No Textbook Recommended, but if you really want to read some
Deng, L., & Liu, Y. (Eds.). (2018). Deep Learning in Natural Language Processing. Springer.
Rao, D., & McMahan, B. (2019). Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning. ” O’Reilly Media, Inc.”.
Manning, C. D., Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT press.

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SELF TEST Ask yourself…
• How much work will you be devoting to this unit, each week?
• Who should you see if difficulties arise?
• When is the first assessment due?
• What do you do if you get sick during semester?
• What is Turnitin?
• What programming language do you need to know?

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What do we learn? (NLP and Deep Learning)

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LANGUAGE
Why Process Language?
• language stores knowledge
• language communicates new knowledge
• language is a key to culture and human experience
• language is a natural interface for humans

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Natural Language Processing (NLP) What is Natural Language Processing?
Natural Language Processing (NLP) is the branch of artificial intelligence focused on developing systems that allow computers to communicate with people using everyday language
Computational Linguistics
It concerns how computational methods can aid the understanding of language
Communication
The goal in the production / comprehension of language is communication.

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Natural Language Processing (NLP) Communication for the speaker:
• Intention: Decide when and what information should be transmitted (a.k.a. strategic generation). May require planning and reasoning about agents’ goals and beliefs.
• Generation: Translate the information to be communicated (in internal logical representation or “language of thought”) into string of words in desired natural language (a.k.a. tactical generation).
• Synthesis: Output the string in desired modality, text or speech.

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Natural Language Processing (NLP) Communication for the hearer:
• Perception: Map input modality to a string of words, e.g. optical character recognition (OCR) or speech recognition.
• Analysis: Determine the information content of the string.
▪ Syntactic interpretation (parsing): Find the correct parse tree showing the
phrase structure of the string.
▪ Semantic Interpretation: Extract the (literal) meaning of the string .
▪ Pragmatic Interpretation: Consider effect of the overall context on altering the literal meaning of a sentence.
• Incorporation: Decide whether or not to believe the content of the string and add it to the Knowledge Base.

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SPECIAL ABOUT NLP What is special about NLP?
• Human language is a system specifically constructed to convey meaning and is not produced by a physical manifestation of any kind. In that way, it is very different from vision or any other machine learning task.
• Most words are just symbols for an extra-linguistic entity : the word is a signifier that maps to a signified (idea or thing).
“Computer” “Apple” “Whaaaaaaa”
???????

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SPECIAL ABOUT NLP Ambiguity
I saw the man on the hill with a telescope.

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SPECIAL ABOUT NLP Ambiguity is Explosive
Ambiguities compound to generate enormous numbers of possible interpretations. In English, a sentence ending in n prepositional phrases has over 2n interpretations.
“I saw the man with the telescope”: 2 parses
“I saw the man on the hill with the telescope.”: 5 parses
“I saw the man on the hill in Texas with the telescope”: 14 parses
“I saw the man on the hill in Texas with the telescope at noon.”: 42 parses
“I saw the man on the hill in Texas with the telescope at noon on Monday” 132 parses

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SPECIAL ABOUT NLP Ambiguity is Ubiquitous
Speech Recognition
• “recognize speech” vs. “wreck a nice beach”
• “youth in Asia” vs. “euthanasia”
Syntactic Analysis
• “I ate spaghetti with chopsticks” vs. “I ate spaghetti with meatballs.”
Semantic Analysis
• “The dog is in the pen.” vs. “The ink is in the pen.”
• “I put the plant in the window” vs. “Ford put the plant in Mexico”

2
SPECIAL ABOUT NLP
Even human struggle with understanding
??????

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SPECIAL ABOUT NLP
The difficulty level in various NLP tasks
Easy
• Spell Checking
• Keyword Search
• Finding Synonyms
Medium
• Extracting Information from documents (including websites)
Difficult
• Semantic Analysis (What is the meaning of query statement?)
• Machine Translation
• Coreference Resolution
• Question Answering

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WORD REPRESENTATION
How to represent the meaning of the word?
Definition: meaning (Collins dictionary).
• the idea that it represents, and which can be explained using other words.
• the thoughts or ideas that are intended to be expressed by it.
Commonest linguistic way of thinking of meaning:
signifier (symbol) ⟺ signified (idea or thing)
“Apple”

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WORD REPRESENTATION
How do we have usable meaning in a computer?
• Common solution: Use e.g. WordNet, a thesaurus containing lists of synonym sets and hypernyms (“is a” relationships).
• http://wordnetweb.princeton.edu/perl/webwn
e.g. synonym sets containing “good”: e.g. hypernyms of “panda”:
from nltk.corpus import wordnet as wn
poses = { ‘n’:’noun’, ‘v’:’verb’, ‘s’:’adj (s)’, ‘a’:’adj’, ‘r’:’adv’}
for synset in wn.synsets(“good”):
print(“{}: {}”.format(poses[synset.pos()],
“, “.join([l.name() for l in synset.lemmas()])))
from nltk.corpus import wordnet as wn panda = wn.synset(“panda.n.01”)
hyper = lambda s: s.hypernyms() list(panda.closure(hyper))
noun: good
noun: good, goodness
noun: good, goodness
noun: commodity, trade_good, good
adj: good
adj (sat): full, good
adj: good
adj (sat): estimable, good, honorable, respectable adj (sat): beneficial, good
adj (sat): good
adj (sat): good, just, upright
…
adverb: well, good
adverb: thoroughly, soundly, good
[Synset(‘procyonid.n.01’), Synset(‘carnivore.n.01’), Synset(‘placental.n.01’), Synset(‘mammal.n.01’), Synset(‘vertebrate.n.01’), Synset(‘chordate.n.01’), Synset(‘animal.n.01’), Synset(‘organism.n.01’), Synset(‘living_thing.n.01’), Synset(‘whole.n.02’), Synset(‘object.n.01’), Synset(‘physical_entity.n.01’), Synset(‘entity.n.01’)]

3
WORD REPRESENTATION Problems with resources like WordNet
• Great as a resource but missing nuance
• e.g. “proficient” is listed as a synonym for “good”. This is only correct in some contexts.
• e.g. “glad” can be synonym for “fXXX off”???
• Missing new meanings of words
• e.g., wicked, badass, nifty, wizard, genius, ninja, bombast
• Impossible to keep up-to-date!
• Subjective
• Requires human labor to create and adapt
• Can’t compute accurate word similarity

4
COUNT based WORD REPRESENTATION One-hot Encoding
• In traditional NLP, we regard words as discrete symbols.
Hot (True) Cold (False)
Words can be represented by one-hot vectors:
1. The categorical values be mapped to integer values (index)
2. each integer value is represented as a binary vector that is all zero values except the index of the integer, which is marked with a 1.
Means one 1, the rest 0s
hotel
motel
Inn
motel = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 0] hotel = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 0] Inn = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 1]
0
1 0
Vector dimension = number of words in vocabulary
1
0 0

4
COUNT based WORD REPRESENTATION
Problem with one-hot vectors
Problem #1. No word similarity representation
Example: in web search, if user searches for “Sydney motel”, we would like to match documents containing “Sydney Inn”
hotel
motel
Inn
motel = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 0]
hotel = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 0]
Inn = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 1]
0
1 0
There is no natural notion of similarity for one-hot vectors!
Problem #2. Inefficiency
Vector dimension = number of words in vocabulary
Each representation has only a single ‘1’ with all remaining 0s.
1
0 0

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COUNT based WORD REPRESENTATION Bag of Words (BOW)
• A bag-of-words model (BoW) is a representation of text that describes the occurrence of words within a document. It involves two things:
• A vocabulary of known words.
• A measure of the presence of known words.
• It is called a “bag” of words, because any information about the order or structure of words in the document is discarded. The model is only concerned with whether known words occur in the document, not where in the document.
How are you How have you been Nice to see you
Have a nice day

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COUNT based WORD REPRESENTATION Bag of Words (BOW)
• A bag-of-words model (BoW) is a representation of text that describes the occurrence of words within a document. It involves two things:
• A vocabulary of known words.
• A measure of the presence of known words.
• It is called a “bag” of words, because any information about the order or structure of words in the document is discarded
How are you How have you been Nice to see you
Have a nice day
Have nice are
How have Nice
a you
been
to
see you you day How

4
COUNT based WORD REPRESENTATION Bag of Words (BOW)
a
are
been
day
have
how
nice
see
to
you
How are you How have you been Nice to see you
Have a nice day
Total Frequency
A vocabulary of known words
* WO = occurrence of words [woa, woare, wobeen, woday, wohave, wohow, wonice, wosee, woto, woyou]
= [0, 1, 0, 0, 0, 1, 0, 0, 0, 1] = [0, 0, 1, 0, 1, 1, 0, 0, 0, 1]
= [0, 0, 0, 0, 0, 0, 1, 1, 1, 1] = [1, 0, 0, 1, 1, 0, 1, 0, 0, 0]
= [1, 1, 1, 1, 2, 2, 2, 1, 1, 3]
a
are
been
day
have
how
nice
see
to
you
1
1
1
1
2
2
2
1
1
3

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COUNT based WORD REPRESENTATION Why use BoW?
• The intuition is that documents are similar if they have similar content. Further, that from the content alone we can learn something about the meaning of the document.
Problem with BoW
• Discarding word order ignores the context, and in turn meaning of words in the document (semantics). Context and meaning can offer a lot to the model, that if modeled could tell the difference between the same words differently arranged (“this is interesting” vs “is this interesting”).
S1= I love you but you hate me S2= I hate you but you love me

4
COUNT based WORD REPRESENTATION Term Frequency-Inverse Document Frequency
•
Term Frequency-Inverse Document Frequency (TF-IDF) is a way of representing how important a word is to a document in a collection or corpus.
wi,j = weight of term i in document j
tfi,j = number of occurrences of term i in document j N = total number of documents
dfi = number of documents containing term i
The Term Frequency is a count of how many times a word occurs in a given document (synonymous with bag of words)
The Document Frequency is the number of times a word occurs in a corpus of documents
• •

4
COUNT based WORD REPRESENTATION Term Frequency
Like BoW
tfi,j = number of occurrences of term i in document j
Document #1: I like apple
Document #2: I like banana
Document #3: Sweet and yellow banana banana Document #4: Sweet fruit
D#2 0 0 1 0 1 1 0 0 D#4 0 0 0 1 0 0 1 0
and apple banana fruit
I like sweet yellow
D#1 0 1 0 0
1100
D#3 1 0 2 0
0011

4
COUNT based WORD REPRESENTATION What if we just use Term Frequency Only?
• It is known that certain terms, such as “is”, “of”, and “that”, may appear a lot of times but have little importance. Thus we need to weigh down the frequent terms while scale up the rare ones.
the language frequency

4
COUNT based WORD REPRESENTATION Can we use Term Frequency Only?
University of Sydney Website
Webpage#1 Webpage#2 Webpage#3

4
COUNT based WORD REPRESENTATION
Inverse Document Frequency
Why do we need log?
N = total number of documents
dfi = number of documents containing term i
Document #1: I like apple
Document #2: I like banana
Document #3: Sweet and yellow banana banana Document #4: Sweet fruit
N=4
and apple
banana fruit
I
like sweet
yellow
df
11
21
2
22
1

4
COUNT based WORD REPRESENTATION
Inverse Document Frequency
Why do we need log?
N = total number of documents
dfi = number of documents containing term i
With log Without log

4
COUNT based WORD REPRESENTATION Inverse Document Frequency
1+dfi sometimes,why?
N = total number of documents
dfi = number of documents containing term i
Document #1: I like apple
Document #2: I like banana
Document #3: Sweet and yellow banana banana Document #4: Sweet fruit
N=4
and apple
banana fruit
I
like sweet
yellow
df
11
21
2
22
1

4
COUNT based WORD REPRESENTATION Inverse Document Frequency
1+dfi
N = total number of documents
dfi = number of documents containing term i
Document #1: I like apple
Document #2: I like banana
Document #3: Sweet and yellow banana banana Document #4: Sweet fruit
idf Inv(4/(1+1)) Inv(4/(1+1)) Inv(4/(2+1)) Inv(4/(1+1)) (with 1+dfi) =0.693147 =0.693147 =0.287682 =0.693147
N=4
and apple
banana fruit
I
like sweet
yellow
df
11
21
2
22
1
Inv(4/(2+1)) =0.287682
Inv(4/(2+1)) =0.287682
Inv(4/(2+1)) =0.287682
Inv(4/(1+1)) =0.693147
We use a natural logarithm to the base of the mathematical constant e.
where e is an irrational and transcendental number approximately equal to 2.718281828459

4
COUNT based WORD REPRESENTATION Term Frequency Inverse Document Frequency
wi,j = weight of term i in document j
Document #1: I like apple
Document #2: I like banana
Document #3: Sweet and yellow banana banana Document #4: Sweet fruit
D#2 0 0 0.287682 0 0.287682 D#4 0 0 0 0.693147 0
0.287682 0 0 0 0.287682 0
1+dfi
and apple
banana fruit
I
like sweet
yellow
D#1
0 0.693147
00
0.287682
0.287682 0
0
D#3
0.693147 0
0.575364 0
0
0 0.287682
0.693147

4
COUNT based WORD REPRESENTATION Sparse Representation
With COUNT based word representation (especially, one-hot vector), linguistic information was represented with sparse representations (high-dimensional features)
hotel
motel Inn
motel = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 0] hotel = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 0] Inn = [0 0 0 0 0 0 0 0 0 0 0 0 0 … 1]
However, with the recent popularity and success of word embeddings (low dimensional, distributed representations), neural-based models have achieved superior results on various language-related tasks as compared to traditional machine learning models with high-dimensional features.
0
1 0
1
0 0

5
NEXT WEEK PREVIEW…
How to Represent the Word Similarity!
• How to represent the word similarity with dense vector
• Try this with word2vec
Reference: http://turbomaze.github.io/word2vecjson/

/
Reference
Reference for this lecture
• Deng, L., & Liu, Y. (Eds.). (2018). Deep Learning in Natural Language Processing. Springer.
• Rao, D., & McMahan, B. (2019). Natural Language Processing with PyTorch: Build Intelligent Language Applications Using Deep Learning. ” O’Reilly Media, Inc.”.
• Manning, C. D., Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT press.
• Manning, C 2017, Introduction and Word Vectors, Natural Language Processing with Deep Learning, lecture notes, Stanford University
• Mooney, RJ 2000, Natural Language Processing Introduction, CS388, lecture notes, University of Texas at Austin

Where to find the information?

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