Compositional Semantics
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1 Compositional Semantics CMSC 723 / LING 723 / INST 725 MARINE CARPUAT marine@cs.umd.edu
2 Words, bag of words Sequences Trees Meaning
3 Representing Meaning An important goal of NLP/AI: convert natural language into a representation that supports semantic inferences Why? Many applications require semantic understanding Question answering, translation, fact-checking, giving instructions to a robot, Challenge: how to bridge gap between linguistic input to non-linguistic knowledge of the world
4 Representing Meaning Challenges for mapping linguistic input to meaning different words/structure, same meaning She needed to make a quick decision in that situation. The scenario required her to make a split-second judgment. I saw the man. The man was seen by me.
5 Representing Meaning Challenges for mapping linguistic input to meaning same words, different meaning - I walked by the bank - to deposit my check. - to take a look at the river. Everyone on the island speaks two languages. Two languages are spoken by everyone on the island.
6 Representing Meaning create representations of linguistic inputs that capture the meanings of those inputs. In most cases, they re simultaneously descriptions of the meanings of utterances and of some potential state of affairs in some world.
7 Desired Properties of Meaning Representations Goal: express propositions, while abstracting away from ambiguity/vagueness of natural language Desired Properties Verifiability No ambiguity Expressiveness Inference
8 Natural Language Inferences Examples All blips are foos. Blop is a blip. Blop is a foo. Mozart was born in Salzburg. Mozart was born in Vienna. No, that can t be. These are different cities.
9 We ll cover different families of approaches Logical Semantics Shallow Representations and Lexical Semantics Textual Inference
10 Constrasting 2 Strategies to Semantic Analysis Logical semantics Complete analysis Create a First Order Logic representation that accounts for all the entities, roles and relations present in a sentence Information Extraction Superficial analysis Pulls out only the entities, relations and roles that are of interest to the consuming application.
11 Information Extraction: Entity Recognition PERSON ORGANIZATION American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said.
12 Information Extraction: Predicting Relations PERSON Founder? Investor? Member? Employee? President? ORGANIZATION American Airlines, a unit of AMR, immediately matched the move, spokesman Tim Wagner said.
13 Information Extraction Relations PERSON- SOCIAL PHYSICAL GENERAL AFFILIATION PART- WHOLE Family Business Lasting Personal Located Near Citizen- Resident- Ethnicity- Religion Subsidiary Org-Location- Origin Geographical Founder Ownership Membership ORG AFFILIATION Sports-Affiliation Investor Student-Alum Employment ARTIFACT User-Owner-Inventor- Manufacturer 17 relations from 2008 Relation Extraction Task from Automated Content Extraction (ACE)
14 Information Extraction Relations UMLS: Unified Medical Language System 134 entity types, 54 relations Injury disrupts Physiological Function Bodily Location location-of Biologic Function Anatomical Structure part-of Organism Pharmacologic Substance causes Pathological Function Pharmacologic Substance treats Pathologic Function
15 Building Blocks of Logical Representations of Meaning Propositional Semantics Proposition symbols: P, Q, Boolean operators negation, conjunction, disjunction Implication, equivalence Inference rules Can be defined using Boolean connectives P => Q
16 Building Blocks of Logical Representations of Meaning Predicate Logic: extends our representation with Constants = elements that name entities in the model Predicates = sets of objects or, equivalently, functions from objects to truth values Functions = sets of pairs of objects, or eq. functions from one object to another
17 Building Blocks of Logical Representations of Meaning Predicate Logic: extends our representation further with Variables = let us refer to objects which are not locally specified Quantifiers = used to bind variables Existential Universal
18 A CFG specification of the syntax of First Order Logic Representations From SLP2 Section 17.3
19 Representing a sentence in FOL Franco likes Frasca. How can we represent the Liking predicateargument template?
20 Predicate-Argument Structure in Natural Language Events, actions and relationships can be captured with representations that consist of predicates and arguments to those predicates. Predicates Primarily Verbs, VPs, Sentences Sometimes Nouns and NPs Arguments Primarily Nouns, Nominals, NPs, PPs But also everything else, depends on the context
21 Example: representing predicateargument structure Mary gave a list to John. Giving(Mary, John, List) More precisely Gave conveys a three-argument predicate The first argument is the subject The second is the recipient, which is conveyed by the NP inside the PP The third argument is the thing given, conveyed by the direct object
22 Example: representing predicateargument structure Predicate-argument structures as templates We can think of the verb/vp providing a template like the following e, x, y, zgiving( e)^giver( e, x)^given( e, y)^givee( e, z) The semantics of the NPs and the PPs in the sentence plug into the slots provided in the template
23 A CFG specification of the syntax of First Order Logic Representations From SLP2 Section 17.3
24 Representing a sentence in FOL Franco likes Frasca. Liking predicate-argument template
25 One More Building Block of Logical Representations of Meaning Lambda forms Take a FOL formula with variables in it that are to be bound. Allow those variables to be bound by treating the lambda form as a function with formal arguments. λx.p(x) λx.p(x)(franco) P(Franco)
26 Lambda Reductions
27 Logical Semantics Representations of Natural Language Building blocks Propositional Logic Predicate Logic Lambda Forms Given a sentence, how can we construct its logical representation? One approach: compositional semantics
28 Compositional Analysis: use syntax to guide semantic analysis
29 Principle of Compositionality The meaning of a whole is derived from the meanings of the parts What parts? The constituents of the syntactic parse of the input What could it mean for a part to have a meaning?
30 Compositional Analysis: use syntax to guide semantic analysis
31 Augmented Rules We ll accomplish this by attaching semantic formation rules to our syntactic CFG rules Abstractly A 1... n { f (α1.sem,...αn.sem)} This should be read as: the semantics we attach to A can be computed from some function applied to the semantics of A s parts.
32 Example Easy parts NP -> PropNoun PropNoun -> Frasca PropNoun -> Franco Attachments {PropNoun.sem} {Frasca} {Franco}
33 Example S -> NP VP VP -> Verb NP Verb -> likes {VP.sem(NP.sem)} {Verb.sem(NP.sem)???
34 Which approach can we use to discover information about specific entities?
35 What approach can we use to summarize text?
36 Which approach can we use to query databases?
37 Which approach can we use to instruct a robot?
38 Recap Intro to Semantics Meaning representations motivated by semantic processing for specific applications 2 approaches to semantic processing complete FOL representation vs. shallow information extraction
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