Usyd大学计算机科学comp5046课程的期末复习

Hello~大家好，今天学姐为留学生分享Usyd大学计算机科学comp5046这门课程辅导，这期的内容主要是分享优秀同学的笔记，学姐整理了非常详细的流程细节可以参考。

课程信息

+ -作业

+ -实验室练习

第一讲

NLP简介

+ -单词表示

++-WordNet

++-一热向量

第二讲

+ -更多单词表示

+ - + -单词包(BoW)

+ - + -术语频率-逆文档频率(TFIDF)

+ -基于预测的单词表示

+ - + - Word2Vec

+ - + -快速文本

+ - + -手套

第三讲

+ -单词嵌入评估

+ -自然语言处理中的深度学习

第四讲

++机器学习和自然语言处理

+ - Seq2Seq学习

+ - + -递归神经网络(RNN)

+ - + -长短期记忆(LSTM)

+ - + -门控循环单元(GRU)

+ - Seq2Seq编码和解码

+ - RNNs

+ -其他

第五讲

+ -聊天机器人

++面向目标的会话代理

++-主动性

++面向聊天的会话代理

++语言基础

++自然语言处理水平

++-文本预处理

第六讲

+ -词性标注

++基线方法

+ -概率方法

+ -深度学习方法

第七讲

+ -依赖结构

++依赖解析算法

+ -评估

第八讲

++语言模型

++-传统语言模型

++神经语言模型

+ -自然语言生成(NLG)

++其他方法

+ -评估

第九讲

+ -信息提取

++命名实体识别(NER)

++-评估

++基于规则的系统

++统计方法

+ - + - Seq2seq代表NER

+ -共同参考分辨率

+ - + -提及配对

+ - + -提到排名

第十讲

+ -关系抽取

++手工构建(基于模式或规则)的方法

++监督方法

++半监督/远程方法

+ -情感分析

第11讲

+ -问答

++基于知识

++基于红外

++-VISual问答

第12讲

+ -机器翻译

++基于规则的机器翻译

++-统计机器翻译

++神经机器翻译

+ -一般来说

++-如何评价机器翻译？

++-机器翻译的总体问题

第十三讲

+ -复习

+ -考试

 

Course information

Assignments

Assignment 1, week 8 Friday

Assignment 2: week 13 Friday

Using

• Python

• Tensorflow, Keras

• Google Colab

Lab exercises

There are 11. You only need to complete 10 to get the whole 10%.

Google Colab provides a free runtime instance for use.

• https://colab.research.google.com/

• Maximum 24 hour runtime

Lecture 1

Introduction to NLP

NLP is about communcation

• Intention

• Generatation

• Synthesis

Why is NLP different to other fields of AI?

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• AI is typically centered around numbers and categories

– Things are clearly defined

• Language has ambiguity

– Words have many meanings

Word representation

The aim is to represent language in a way we can feed to a machine. Making

language discrete.

WordNet

• Building a word network (words linked using synoyms)

Problems with solutions like Wordnet

• Nuance of words is lost with synoyms

• Language changes over time

• Requires human effort to maintain, add new words

One-hot vectors

• Represent words as a sparse vector

• One-hot vectors are vectors with a single 1 value, everything else 0 value

Problems with one-hot vectors

• Inefficient

– Vector dimension equals number of words in vocabulary

• No natural idea of word similarity

– All one-hot vectors are orthogonal

• Instead: encode similarity in the vector

– Build a dense vector

Lecture 02

More word representation

Bag of words (BoW)

• All words in a corpus are thrown into a bag

• From this bag, we can derive the vocabulary and a measure of how often

the words appear (occurence)

• It does not care about the order of the original corpus

Problem with bag of words approach

• Meaning is in the order of the words

– “this is interesting” vs “is this interesting?”

4

Term frequency-inverse document frequency (TFIDF)

• Term frequency is the the number of times a words occurs in a given

document

• Inverse document frequency is the number of times a word occurs in a

corpus (many documents)

Prediction-based word representation

Word2Vec

• Considers context

– It looks at the set of words which surround the center word

• Two models

– Continuous bag of words (CBOW)

– Skip-gram

CBOW Model

• Predict the center word from a bag of context words

• Context words are input to the neural network

Skip-gram Model

• Predict the context words from the center word

• Works better with infrequent words

• Centre word is input for a neural network and the output is the context

words

Limitations

• Cannot cover morphological similarities

– e.g. teach, teaching, teacher are treated as completely different words

• Hard to predict rare words

– The NN is example-based. It is underfitting.

• Cannot handle words out-of-vocabulary (OOV)

• If the word is spelled the same, it is considered the same word (homonyms)

FastText

• Word2Vec but with n-grams

• Deals better with rare and OOV words

– because it is likely that part of the new word has been seen before in

the training corpus

• Focus on local context windows, rather than word occurrence

GloVe

• Both Word2Vec and FastText consider context words local to a center

word

5

– This is performed one window at a time

– It does not consider anything beyond the local scope

• GLOVE builds a co-occurence matrix

– Counts how often a word appears in a context

– Performs dimenionality-reduction on the matrix (e.g PCA)

Training data reflects the prediction result.

• Training NN on Google News will produce different machine model to

training NN on Twitter data

Lecture 03

Recap:

• Word2Vec is based on a sliding window of words

– Predicting center word = CBOW

– Predicting context words = Skipgram

• Fasttext can deal with unseen words by applying n-grams

Word embedding evaluation

Intrinsic, extrinsic

• Intrinsic: Evaluate the embedding model on a specific subtask, or intermediate subtask

– For example, if you are building a Question-Answering system, instrinsic evaluation would be evaluating the word-embedding component

individually to assess how well it performs

– Fast to compute

– Unclear if the real task is anything like the subtask task

• Extrinsic: Evaluate the model on a real task

– For example, if you are building a Question-Answering syste, extrinsic evaluation would be evaluating the word-embedding model by

evaluating the entire QA system

– Can take a long time to compute

– Unclear if it interacts with other systems

Deeplearning in NLP

An neuron has

• Function with parameters

• Cost and optimiser functions

Parameters and hyper-parameters

• Parameters

– They are tunable

6

– They are learned from training data

• Hyper-parameters

– Variables controlling how parameters are learned

– e.g. Learning rate, model size/depth

Lecture 04

Machine learning and NLP

NLP methodology

 

 

Figure 2: N:M Mapping of problems in NLP

7

Seq2Seq learning

Given a sequence, generate a sequence

Examples

• PoS tagging — words to part of speech

• Speech to text — frames to words

• Movie frame labelling — frames to labels

• Machine translation — words to words

• Sentence completion — words to single word (autocomplete)

Recurrent NN (RNN)

Recurrent (read: re-current. . . last output concatenated with the new input)

• Input is not aware of future inputs

• Vanishing gradient, limited long term memory

– Data input a long time ago may be lost in future noise

Long short-term memory (LSTM)

• Each cell maintains cell state

– Memory cell decide when new information enters, when it is output,

and when it is forgotten (using input, output, and forget gates)

• Computationally intensive because of many gates/calculations

Gated Recurrent Unit (GRU)

• Similar to LSTM, but does not cell state

– Fewer gates (update and reset gate)

– Faster computations

• GRU is recommended if you have a larger dataset because it is faster to

train

Many of the GIFs in the lecture slides are sourced from here: https://towardsdatascience.com/illustratedguide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21

Seq2Seq encoding and decoding

How to reduce the dimensionality of your data?

• Multiply with a weighted vector (kernel)

• We can generalise by adding more kernels

How do add context data to the input of your NN?

• Merge the input data with the context data, OR

• Output the context to the dimensionality as your expected output and

merge with the new input

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