TEORIE E TECNICHE DEL RICONOSCIMENTO Linguistica computazionale in Python: -Analisi sintattica (parsing)

DAL CHUNKING ALL’ANALISI SINTATTICA COMPLETA

PROBLEMA: AMBIGUITA’ While hunting in Africa, I shot an elephant in my pajamas. How an elephant got into my pajamas I'll never know.

PROBLEMA: AMBIGUITA’ While hunting in Africa, I shot an elephant in my pajamas. How an elephant got into my pajamas I'll never know.

CARATTERIZZAZIONE DELLA SINTASSI DI UNA LINGUA: CONTEXT-FREE GRAMMARS Slides ELN?

CARATTERIZZAZIONE DELLA SINTASSI DI UNA LINGUA: CONTEXT-FREE GRAMMARS Capture constituency and ordering – Ordering: What are the rules that govern the ordering of words and bigger units in the language? – Constituency: How words group into units and how the various kinds of units behave

Constituency E.g., Noun phrases (NPs) Three parties from Brooklyn A high-class spot such as Mindy’s The Broadway coppers They Harry the Horse The reason he comes into the Hot Box How do we know these form a constituent?

Constituency (II) – They can all appear before a verb: Three parties from Brooklyn arrive… A high-class spot such as Mindy’s attracts… The Broadway coppers love… They sit – But individual words can’t always appear before verbs: *from arrive… *as attracts… *the is *spot is… – Must be able to state generalizations like: Noun phrases occur before verbs

Constituency (III) Preposing and postposing: – On September 17th, I’d like to fly from Atlanta to Denver – I’d like to fly on September 17th from Atlanta to Denver – I’d like to fly from Atlanta to Denver on September 17th. But not: – *On September, I’d like to fly 17th from Atlanta to Denver – *On I’d like to fly September 17th from Atlanta to Denver

Indicating constituents: brackets, trees [ S [ NP [ PRO I]] [ VP [ V prefer] [ NP [ Det a] [ Nom [ N morning] [ N flight] ] ] ] ] S NPVP NP VerbPro Nom DetNoun Iprefermorningaflight

CFG example S -> NP VP NP -> Det NOMINAL NOMINAL -> Noun VP -> Verb Det -> a Noun -> flight Verb -> left

NLE12 Beyond regular languages: Context- Free Grammars S  NP VP NP  Det Nominal Nominal  Noun VP  V Det  the Det  a Noun  flight V  left

CFGs: set of rules S -> NP VP – This says that there are units called S, NP, and VP in this language – That an S consists of an NP followed immediately by a VP – Doesn’t say that that’s the only kind of S – Nor does it say that this is the only place that NPs and VPs occur

Generativity As with FSAs you can view these rules as either analysis or synthesis machines – Generate strings in the language – Reject strings not in the language – Impose structures (trees) on strings in the language How can we define grammatical vs. ungrammatical sentences?

Derivations A derivation is a sequence of rules applied to a string that accounts for that string – Covers all the elements in the string – Covers only the elements in the string

Derivations as Trees S NPVP NP VerbPro Nom DetNoun Iprefermorningaflight

CFGs more formally A context-free grammar has 4 parameters (“is a 4-tuple”) 1)A set of non-terminal symbols (“variables”) N 2)A set of terminal symbols  (disjoint from N) 3)A set of productions P, each of the form A ->  Where A is a non-terminal and  is a string of symbols from the infinite set of strings (   N)* 4)A designated start symbol S

Defining a CF language via derivation A string A derives a string B if – A can be rewritten as B via some series of rule applications More formally: – If A ->  is a production of P –  and  are any strings in the set (   N)* – Then we say that  A  directly derives  or  A    – Derivation is a generalization of direct derivation – Let  1,  2, …  m be strings in (   N)*, m>= 1, s.t.  1   2,  2   3 …  m-1   m We say that  1 derives  m or  1*   m – We then formally define language L G generated by grammar G A set of strings composed of terminal symbols derived from S L G = {w | w is in  * and S *  w}

NLE19 Derivations A DERIVATION of a string is a sequence of rule applications – E.g., the string “a flight” can be derived from the grammar above and symbol NP by the (leftmost first) derivation NP => Det Nominal => a Nominal => a Noun => a flight Derivations can be visualized as PARSE TREES The LANGUAGE defined by a CFG is the set of strings derivable from the start symbol S (for Sentence)

NLE20 Derivations and parse trees

NLE 21 A more formal definition A CFG is a 4-tuple consisting of

NLE22 What `context free’ means

NLE23 Derivations and languages The language L G GENERATED by a CFG grammar G is the set of strings of TERMINAL symbols that can be derived from the start symbol S using the production rules in G – L G = {w | w is in  * and S derives w} The strings in L G are called GRAMMATICAL The strings not in L G are called UNGRAMMATICAL

NLE24 Grammar development One of the most basic skills in NLE is the ability to write a CFG for some fragment of a language (e.g., the dates) We’ll briefly cover some of the issues to be addressed when writing small CFG grammars

CFG in PYTHON NLTK, 8.3

ANALISI SINTATTICA TOP-DOWN search: the parse tree has to be rooted in the start symbol S – EXPECTATION-DRIVEN parsing – Esempio; RECURSIVE DESCENT BOTTOM-UP search: the parse tree must be an analysis of the input – DATA-DRIVEN parsing – Esempio: SHIFT-REDUCE

TOP-DOWN PARSING CON NLTK Recursive descent parsing (NLTK, 8.3) – nltk.RecursiveDescentParser(grammar) – nltk.app.rdparser()

BOTTOM-UP PARSING CON NLTK Shift-reduce (NLTK, 8.3, p. 305) – nltk.app.srparser() – ShiftReduceParser(grammar)

MODELLI PIU’ AVANZATI DI PARSING Left corner (NLTK) Chart (NLTK)

DEPENDENCIES E DEPENDENCY GRAMMAR (NLTK, 8.5)

IL PROBLEMA DELL’AMBIGUITA’ Ambiguity – Church and Patel (1982): the number of attachment ambiguities grows like the Catalan numbers C(2) = 2, C(3) = 5, C(4) = 14, C(5) = 132, C(6) = 469, C(7) = 1430, C(8) = 4867 Avoiding reparsing

COMMON STRUCTURAL AMBIGUITIES COORDINATION ambiguity – OLD (MEN AND WOMEN) vs (OLD MEN) AND WOMEN ATTACHMENT ambiguity: – Gerundive VP attachment ambiguity I saw the Eiffel Tower flying to Paris – PP attachment ambiguity I shot an elephant in my pajamas

PP ATTACHMENT AMBIGUITY

AMBIGUITY: SOLUTIONS Use a PROBABILISTIC GRAMMAR (not covered in this module) Use semantics

SCRIVERE UNA GRAMMATICA NLTK, 8.6