# Logic Form Representations Reading: Chap 14, Jurafsky & Martin Slide set adapted from Vasile Rus, U. Memphis Instructor: Rada Mihalcea.

## Presentation on theme: "Logic Form Representations Reading: Chap 14, Jurafsky & Martin Slide set adapted from Vasile Rus, U. Memphis Instructor: Rada Mihalcea."— Presentation transcript:

Logic Form Representations Reading: Chap 14, Jurafsky & Martin Slide set adapted from Vasile Rus, U. Memphis Instructor: Rada Mihalcea

Slide 1 Problem Description There is need for Knowledge Bases E.g.: Question Answering 1. find the answer to Q471: What year did Hitler die? in a collection of documents A: “ Hitler committed suicide in 1945 ” 2. how would one justify that it is the right answer: using world knowledge suicide – {kill yourself} kill – {cause to die} Create intelligent interfaces to databases: E.g.: Where can I eat Italian food? Or: I'd like some pizza for dinner. Where can I go?

Slide 1 How to Build Knowledge Bases? Manually - building common sense knowledge bases - see Cyc, Open Mind Common Sense Automatically - from open text - from dictionaries like WordNet

Slide 1 Logic Form Representation What representation to use? Logic Form (LF) is a knowledge representation introduced by Jerry Hobbs (1983) Logic form is a first-order representation based on natural language

Slide 1 First Order Representations Fulfil the five main desiderata for representing meaning: 1. Verifiability: Does Maharani serve vegetarian food? Serves(Maharani, vegetarian food) A representation that can be used to match a proposition against a knowledge base 2. Unambiguous representations: I would like to eat someplace close to UNT. = eat in a place near UNT = eat a place Get rid of ambiguity by assigning a sense to words, or by adding additional information that rules out ambiguity. A representation should be free of ambiguity.

Slide 1 First Order Representations 3. Canonical Form Does Maharani serve vegetarian food? Are vegetarian dishes served at Maharani? Do they have vegetarian food at Maharani? Texts that have the same meaning should have the same representation. 4. Inference and Variables The ability to draw inferences from the representations Serves(x, Vegetarian Food) --> EatAt(Vegetarians, x) 5. Expresiveness Representations should be expressive enough to handle a wide range of subjects.

Slide 1 Induction, Abduction Use FOP for automatic reasoning How? Induction Abduction

Slide 1 Logic Form Transformations First order representations - have the characteristics of FOP Add some extra information (e.g. POS, word sense) Derived automatically from text, starting with parse trees Used for automatic construction of knowledge bases: - e.g. Starting with WordNet

Slide 1 WordNet as a Source of World Knowledge [review] WordNet, developed at Princeton by Prof. Miller, is an electronic semantic network whose main element is the synset – synset – a set of synonym words that define a concept E.g.: {cocoa, chocolate, hot chocolate} a word may belong to more than one synset WordNet contains synsets for four parts of speech: noun, verb, adjective and adverb synsets are related to each other via a set of relations: hypernymy (ISA), hyponymy(reverseISA), cause, entailment, meronymy(PART-OF) and others. hypernymy is the most important relation which organizes concepts in a hierarchy (see next slide) adjectives and adverbs are organized in clusters based on similarity and antonymy relations

Slide 1 WordNet glosses Each synset includes a small textual definition and one or more examples that form a gloss. E.g.: – {suicide:n#1} – {killing yourself} – {kill:v#1} – {cause to die} – {extremity, appendage, member} – {an external body part that projects from the body “ it is important to keep the extremities warm ” } Glosses are a rich source of world knowledge Can transform glosses into a computational representation

Slide 1 Logic Form Representation A predicate is a concatenation of the morpheme ’ s base form, part of speech and WordNet semantic sense – morpheme:POS#sense(list_of_arguments) There are two types of arguments: – x – for entities – e – for events The position of the arguments is important – verb:v#sense(e, subject, direct_object, indirect_object) – preposition(head, prepositional_object) A predicate is generated for each noun, verb, adjective and adverb Complex nominals are represented using the predicate nn: – e.g.: “ goat hair ” – nn(x1, x2, x3) & goat(x2) & hair(x3) The logic form of a sentence is the conjunction of individual predicates

Slide 1 An Example {lawbreaker, violator}: (someone who breaks the law) Someone:n#1(x1) & break:v#6(e1, x1, x2) & law:n#1(x2) Part of Speech WordNet sense SubjectDirect object Categorial Information Semantic Information Functional Information

Slide 1 Logic Form Notation (cont ’ d) Ignores: plurals and sets, verb tenses, auxiliaries, negation, quantifiers, comparatives Consequence: – Glosses with comparatives can not be fully transformed in logic forms The original notation does not handle special cases of postmodifiers (modifiers placed after modifee) respectively relative adverbs (where, when, how, why)

Slide 1 Comparatives {tower}: (structure taller than its diameter) taller/JJR modifies structure or diameter? Both? Solution: introduce a relation between structure and diameter LF: structure(x1) & taller(x1, x2) & diameter(x2)

Slide 1 Postmodifiers {achromatic_lens}: (a compound lens system that forms an image free from chromatic_aberration) Free is a modifier of image ? What is the prepositional head of from ? Solution: free_from – NEW predicate LF: image(x1) & free_from(x1, x2) & chromatic_aberration(x2)

Slide 1 Relative Adverbs {airdock}: (a large building at an airport where aircraft can be stored) Equivalent to: (aircraft can be stored in a large building at an airport) LF: large(x1) & building(x1) & at(x1, x2) & airport(x2) & where(x1, e1) & aircraft(x3) & store(e1, x4, x3)

Slide 1 Logic Form Identification Take advantage of the structural information embedded in a parse tree NP VP S -> NP VP NP VP-PASS NP VP-ACT Direct object Subject S Preprocess (Extract Defs, Tokenize) POS Tag Parse LF Transformer Architecture

Slide 1 Example of Logic Form NP VP DTNNVBNPP IN NP DTNN a monastery ruled by an abbot monastery:n(x1) rule:v(e1, x2, x1) abbot:n(x2)

Slide 1 Logic Form Derivation Take advantage of the syntactic information from the parser For each grammar rule derive one or more LF identification rules {abbey:n#3}(VP (ruled/VBN by/PP)) Verb(e, -, -)/VP-PASS by/PP(-,x)  verb(e,x, -) & by(e,x) VP  VP PP {abbey:n#3}(NP (a/DT monastery/NP)) Noun/NN  noun(x)NP  DT NN SynsetPhraseRuleGrammar Rule Identification Rules NP DTNN VP PP

Slide 1 Building Logic Forms from WordNet From definitions to axioms WordNet glosses transformed into axioms, to enable automated reasoning Specific rules to derive axioms for each part of speech: – Nouns: the noun definition consists of a genus and differentia. The generic axiom is: concept(x)  genus(x) & differentia(x). E.g.: abbey(x1)  monastery(x1) & rule(e1, x2, x1) & abbot(x2) – Verbs: are more trickier as some syntactic functional changes can occur from the left hand side to the right hand side E.g.: kill:v#1(e1, x1, x2, x3)  cause(e2, x1, e3, x3) & die(e3, x2) – Adjectives: they borrow a virtual argument representing the head they modify E.g.: american:a#1(x1)  of(x1, x2) & United_States_Of_America(x2) – Adverbs: the argument of an adverb borrows a virtual event argument as they usually modify an event E.g: fast:r#1(e1)  quickly:r#1(e1)

Slide 1 Building a Knowledge Base from WordNet Parse all glosses and extract all grammar rules embedded in the parse trees The grammar is large If we consider that a grammar rule can map in more than one LF rules the effort to analyse and implement all of them would be tremendous 9,826Total 639Adverbs 1,958Adjectives 1,837Verb 5,392Noun RulesPart of speech

Slide 1 Coverage issue Group the grammar rules by the non terminal on the Left Hand Side (LHS) and notice that the most frequent rules for some class cover most of the occurrences of rules belonging to that class The coverage of top 10 most frequent grammar rule for phrases as measured in 10,000 noun glosses. What does this remind you of? 99%4012,315PP 99%3514,740S 70%45019,415VP 95%24411,408NP 69%85733,643Base NP Coverage of top tenUnique RulesOccurrencesPhrase on the LHS of Grammar Rule

Slide 1 Coverage issue (cont ’ d) Two phases: – Phase 1: develop LF rules for most frequent rules and ignore the others – Phase 2: select more valuable rules The accuracy of each LF rule is almost perfect The performance issue is mainly about how many glosses are entirely transformed into LF i.e. how many glosses the selected grammar rules fully map into LF

Slide 1 Reduce the number of candidate grammar rules (1) Selected grammar rules for baseNPs (non-recursive NPs) have only a coverage of 69% Selected grammar rules for VPs have only 70% coverage Before selecting rules for baseNPs we make some transformations to reduce more complex ones to simpler ones Coordinated base NPs are transformed into coordinated NPs and simple base NPs NP DTNNCCNN NPCCNP DTNN a ruler or institution

Slide 1 Reduce the number of candidate grammar rules (2) Base Nps: – Determiners are ignored (an increase of 11% in coverage for selected grammar rules for base NPs) – Plurals are ignored – Everything in a prenominal position plays the role of a modifier VPs: – Negation is ignored – Tenses are ignored (auxiliaries and modals) NP  DT VBN NN| NNS | NNP|NNPS NP  DT VBG NN|NNS|NNP|NNPS NP  DT JJ NN|NNS|NNP|NNPS Base NP rule

Slide 1 Map grammar rules into LF rules Selected grammar rules map into one or more Logic Form rules Case 1: grammar rule is mapped into one LF rule – Grammar rule: PP -> IN NP – LFT: prep(_, x)  prep(_, x) & headNP(x) Case 2: grammar rule is mapped into one or more LF rules: – Grammar rule: VP -> VP PP – LFT 1: verb(e, x1, _)  verb-PASS(e,x1, _) & prep-By(e, x1) – LFT 2: verb(e, _, x2)  verb-PASS(e, _, x2) & prep-nonBy(e, x2) – To differentiate among the two cases we use two features: The mood of the VP: active or passive The type of preposition: by or non-by

Slide 1 Logic Form Derivation Results Phase 1: – From a corpus of 10,000 noun glosses extract grammar rules, sort them by the nonterminal on the LHS, select the most frequent grammar rules and generate LF rules for them – Manually develop a test corpus of 400 glosses – Test the implemented LF rules on 400 noun glosses – 72% coverage (with almost 100% accuracy) Phase 2: – Select iteratively more rules that bring an increase in coverage of at least  – For glosses  was established at 1% This resulted in a total number of 70 grammar rules selected The new coverage achieved is 81% Open issue: how to fully cover the remaining 19% of glosses which are not fully transformed – using a set of heuristics E.g.: if the subject argument of a verb is missing use the first previous noun as its subject