Computational Linguistics Yoad Winter *General overview *Examples: Transducers; Stanford Parser;...
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Transcript of Computational Linguistics Yoad Winter *General overview *Examples: Transducers; Stanford Parser;...
Computational Linguistics Yoad Winter
* General overview
* Examples: Transducers; Stanford Parser; Google Translate; Word-Sense
Disambiguation
* Finite State Automata and Formal Grammars
Linguistics - from Theory to Technology
Computational Linguistics
Theoretical Linguistics
Natural Language Processing
Language Technology
INFO
TLW
Industrie
Goals of CL:* Foundations for Linguistics in Computer Science (e.g. Formal Language theory)* Computable linguistic theories (HPSG, LFG,
Categorial Grammar)* Implementation of demos for linguistic
theories* (Mathematical Linguistics)
Computational Linguistics
Goals of NLP – practical applications of CL:* Speech recognition/synthesis* Machine translation* Summarization* Question answering* Text categorization* Grammar checking
Statistical NLP:* Unsupervised* Supervised (corpus-based)
Natural Language Processing
Goals of LT:* Useful linguistic resources (lexicons, grammar
rules, semantics webs)* Implementation of most useful tools involving
language processing(Google translation, Word spell checker, MS Speech Recognizer etc.)
Language Technology
Computational Linguistics
Theoretical Linguistics
Natural Language Processing
Language Technology
Input: Output: J&M (2009)
Words in text Part of speech (Noun/Verb); Morphological Information
Speech Sound TextWave
text Speech Sound Wave
Sentence in text Phrases in Sentences (noun phrase, verb phrase)
Sentence/text Action/Reasoning
Sentence/text Translation
Language Processing - Tasks
I: Words
II: Speech
III: Syntax
IV: Semantics & Pragmatics
V: Applications
Start with null information state I=0Repeat while there is language to read:
- Read a language token T- Recognize T: extract information
I(T) - Update information state I using I(T)- Do some action using I(T)
Processing - General Idea
I want to cash a check:- Start from state 0- Read “I want to”, move to state 1, and
output nothing- Read “cash”, move to state 3, and output V- Read “a check”, move to state 4, and
output nothing
Example 1 – Finite State Transducers
I want to have some |ε
I want to |ε cash | V
cash | Na check |ε0
2
1 3
4
I want to have some cash:- Start from state 0- Read “I want to have some”, move to state
2, and output nothing- Read “cash”, move to state 4, and output N
Example 1 – Finite State Transducers
I want to have some |ε
I want to |ε cash | V
cash | Na check |ε0
2
1 3
4
Your queryI want to cash a checkTaggingI/PRP want/VBP to/TO cash/VB a/DT check/NNParse
Example 2 – Stanford Parser
http://nlp.stanford.edu/software/lex-parser.shtml link
Your queryI want to have some cashTaggingI/PRP want/VBP to/TO have/VB some/DT cash/NNParse
Example 2 – Stanford Parser
http://nlp.stanford.edu/software/lex-parser.shtml link
Example 3 – Google Translate
Example 3 – Google Translate
Summary
We have seen ways to process:- words, word-by-word:
transducers- sentences, with a tree structure:
Stanford Parser
A word like CASH must be disambiguated for Noun or Verb, in order to have a correct translation.
Other kinds of disambiguation?
Example 4 - Word-Sense Disambiguation
the light blue car: 1. de lichtblauwe auto2. de lichte blauwe auto
John likes the light blue car but not the deep blue car
John was able to lift the light blue car but not the heavy blue car
Google Translate: lichtblauwe auto in both cases
Word-Sense disambiguation: finding the right sense of the word
Basic Model 1: Finite State Automata (FSA)
q0- start stateq4- accepting statearrows – transitions, also defined by a transition
table
FSA - formally
Tracing the execution of an FSA
“baaa!” is accepted because when taking the input symbols one by one, we reached the accepting state q4.
FSA’s as Grammars
An FSA possibly describes an infinite set of strings over a finite input alphabet Σ.
We thus say that an FSA describes a grammar over Σ, which derives a formal language over Σ.
More officially:Σ – a finite set Σ* – all the strings over Σ (infinite)L(FSA) = the language of the FSA
is the set of strings S in Σ* that are derived by the FSA.
Any set described by an FSA is called regular.
Non-regular languages and complexity
L = { ab, aabb, aaabbb, aaaabbbb, … }can be shown to be non-regular.
No FSA can derive this language L!
But there are grammars that can also generate non-regular languages!
Are natural languages regular or non-regular?How hard it is for a computer to recognize regular
and non-regular languages?Are there different classes of formal languages in
terms of their complexity?
Another way to define regular langauges – regular expressions
A regular expression is a compact way for describing a regular language.
Example: baa(a*)!
descibes the same language as the FSA we saw.We say that this regular expression matches any string in this
language, and does not match other strings.
Regular expressions - formally
Σ - a finite alphabet1- Any string in Σ is a regular expression that matches
itself“a” matches “a”; “b” matches “b”; etc.
2- If A and B are regular expressions then AB is a regular expression that matches any concatenation of a string that A matches with a string that B matches.
“ab” matches “ab”3- If A and B are regular expressions then A|B is a regular
expression that matches any string that A or B match. “a|b” matches both “a” and “b”
4- If A is regular expression then A* matches any string that has zero or more As.
“a*” matches the empty string, “a”, “aa”, “aaa” etc.
Examples
Convention: we give precedence to *.AB* = A(B*)
Convenience: we let ε match the empty string.
a|b* matches {ε, "a", "b", "bb", "bbb", ...}
(a|b)* matches the set of all strings with no symbols other than "a" and "b", including the empty string: {ε, "a", "b", "aa", "ab", "ba", "bb", "aaa", ...}
ab*(c|ε) denotes the set of strings starting with "a", then zero or more "b"s and finally optionally a "c": {"a", "ac", "ab", "abc", "abb", "abbc", ...}
At home
Read 3.4-3.6 on Transducers as preparation for Eva’s class.