1. Computational Linguistics Yoad Winter *General overview *Examples: Transducers; Stanford Parser; Google Translate; Word-Sense Disambiguation * Finite State Automata and Formal Grammars

2. Linguistics - from Theory to Technology Computational Linguistics Theoretical Linguistics Natural Language Processing Language Technology INFO TLW Industrie

3. 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

4. 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

5. 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

6. Input:Output: J&M (2009) Words in textPart of speech (Noun/Verb); Morphological Information Speech Sound Text Wave textSpeech Sound Wave Sentence in textPhrases in Sentences (noun phrase, verb phrase) Sentence/textAction/Reasoning Sentence/textTranslation Language Processing - Tasks I: Words II: Speech III: Syntax IV: Semantics & Pragmatics V: Applications

7. Start with null information state I=0 Repeat 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

8. 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 | N a check | ε 0 2 13 4

9. 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 | N a check | ε 0 2 13 4

10. Your query I want to cash a check Tagging I/PRP want/VBP to/TO cash/VB a/DT check/NN Parse Example 2 – Stanford Parser http://nlp.stanford.edu/software/lex-parser.shtml linklink

11. Your query I want to have some cash Tagging I/PRP want/VBP to/TO have/VB some/DT cash/NN Parse Example 2 – Stanford Parser http://nlp.stanford.edu/software/lex-parser.shtml linklink

12. Example 3 – Google Translate

14. 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?

15. Example 4 - Word-Sense Disambiguation the light blue car: 1. de lichtblauwe auto 2.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

16. Basic Model 1: Finite State Automata (FSA) q0- start state q4- accepting state arrows – transitions, also defined by a transition table

17. FSA - formally

18. Tracing the execution of an FSA “baaa!” is accepted because when taking the input symbols one by one, we reached the accepting state q4.

19. 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.

20. 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?

21. 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.

22. Regular expressions - formally Σ - a finite alphabet 1- 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.

23. 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",...}

24. At home Read 3.4-3.6 on Transducers as preparation for Eva’s class.