A research field focussed on creating software systems with knowledge about natural (human) language 研究重點是關於自認語言的知識
Interdisciplinary: makes use of theories from Linguistics 語言學理論 , adopts an Engineering approach
Aimed at human-like understanding of language (but not yet there)
!tip\] Contributing disciplines
Linguistics: formal models of language, linguistic knowledge
Computer Science: representations, efficient processing, state machines, parsing
algorithms, probabilistic models, dynamic programming, machine learning
Mathematics: formal automata theory, computational modelling
Psychology: psychologically plausible modelling of language use
Many types of ambiguity歧義:
Phonological 語音學
multiple interpretations due to how it sounds 有些音聽起來一樣 那麼在識別的時候會存在很多種解釋
Lexical 詞匯
multiple interpretations due to a word having multiple senses 詞語的歧義，由於有的單詞本身帶有的意思多重導致的
Syntactic 句法
due to a word having more than one possible part of speech 一個單詞有多個演講部分
due to prepositional phrase attachment 介詞的附件
Semantic 語義
multiple possible interpretations解釋 unless knowledge of the world is available
!danger\] Two major approaches to NLP
* Symbolic 象征
* Rule- and dictionary-based systems 基於規則和字典系統
* Captures linguistic knowledge in rules written by experts 補貨專家撰寫的規則中的語言知識
* Statistical/Machine learning-based
* Data-driven 數據驅動
* Use of large amounts of (labelled) textual data (文本數據) to train systems, **discover patterns**
Comparison
✅Can generalise well on unseen examples可以很好的概括在看不見的例子上
❎Shortage of experts
❎Need people for labelling
❎Laborious rule writing, dictionary preparation
❎Time consuming and laborious labelling 耗時且費力
❎ Domain adaptation problematic域適應性問題
❎Must retrain for new domain必須重新訓練
✅Results can be interpreted結果可以解釋
❎ Often cannot inspect/change models通常無法檢查/更改模型
✅ Good when labelled data is hard to obtain當很難獲得標記的數據時
✅ Good where dictionaries are unavailable
NLP Pipelines
A 'complete' NLP system is usually a pipeline of components
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Sentence Segmentation 句子細分
Tokenisation 象征化
Parsing
Information
Sentence segmentation (split)
Tokenisation (split)
Named entity recognition (combine)
Why is NLP challenging:
Natural Language evolve:
new words appear constantly
Syntactic rules are flexible 句法規則靈活
ambiguity (模糊性) is inherent 固有的
Why Machine Learning for NLP
Traditional rule-based artificial intelligence (symbolic AI):
requires expert knowledge to engineer the rules 需要專家知識來設計
not flexible to adapt in multiple languages, domains, applications 不能靈活低使用與多種語言
Learning from data (machine learning) adapts:
to evolution: just learn from new data 從新數據中學習
to different applications: just learn with the appropriate target representation
Sentence Segmentation 句子細分
Sentence Detection is done before the text is tokenized 句子檢測在文本標記化之前進行
Task 順序：
Determination of boundaries between sentences確定句子之間的界限
Sentences used in subsequent NLP tasks 隨後NLP中使用的句子
Is it enough to detect the full stop
Could be an end-of-sentence (EOS) marker 可能是句子結束 (EOS) 標記
Or an end of abbreviation marker 縮寫標記的結尾
Or both
通常是Text Mining的第一步，因為它將非結構化文本拆分成基本處理單位
Variation in delimiters
Typical：".","!","?"
!todo\] # Approaches
* Regular expressions (Patterns) 正則表達式
* Dictionaries (e.g., abbreviation lists) 字典
* Hand-crafted rules 手工製作的規則(e.g., to check whether the word following an EOS delimiter starts with an uppercase character 檢查EOS界定符之後的單詞是否從大寫字符開始)
* Statistical and ML approaches
* Hybrid approaches 混合方法
Example of useful rules or Features
First character after potential EOS char 潛在EOS Char之後的第一個字符
● Should be uppercase? Problematic for some languages, e.g. German
● Permissible chars after potential EOS, e.g. lowercase characters?
Abbreviations 縮寫
● titles not likely to occur at EOS (e.g., Dr. Jones)
● company indicators could occur at EOS (e.g., MySocialMedia Inc.)
