From Syntax to Semantics How to get from Form to Meaning in Two different ways

What is meaning? Connection (grounding) in something outside itself Mental concept (ideas) Objects and events in the world (true/false) Some combination of the above Ultimately – the success of the program in which it is embedded

Principle of Compositionality The meaning of the whole is derived from the meaning of the parts and the manner of their combination {John, kiss, Sally} John kissed Sally. Sally kissed John.

Semantics -- For our purposes Formal representational language that represents the “manner of combination” Lexicon that connects lexical items with some externally grounded object, the “meaning of the parts”

Two approaches Logical Interlingual To some extent complementary Language of formal logic Model (set) theoretic grounding Interlingual Specially-developed InterLingual (IL) Representation Ontology to represent word meaning To some extent complementary

Logical approach Predicate calculus and model theory PLUS Extra stuff to handle some of the complexities of natural language, such as (Scope) Every man loves a woman. (Generics) Dogs have four legs. (Specificity) John wants to marry a Norwegian. (Intension) What if all bald men are tall? (Roles) The temperature is ninety and rising.

Logical approach – λ calculus Key idea: semantic construction parallels syntactic construction John = john’ sleep = sleep’ John is sleeping = sleep’(john’) sleep = λx[sleep’(x)] John is sleeping = λx[sleep’(x)](john’) Lambda conversion = sleep’(john’)

Logical approach – possible worlds Instead of one model – many models Each model is a “possible world” – one is designated as “real” Temporal logic Modal logic Intensional logic

IL approach Developed in the context of Machine Translation Interested in word sense disambiguation The pig is in the pen. The ink is in the pen. Non-literal language: metonymy/metaphor “The White House reported today that …” “The business opened its doors in 1928.” Inferencing for translation mismatches

IL approach An Ontology, a language-independent classification of objects, event, relations A Semantic Lexicon, which connects lexical items to nodes (concepts) in the ontology An analyzer that constructs IL representations and selects (an?) appropriate one

IL approach – Ontology A classification tree in which mother node contains all below it, and daughter nodes are distinct (is-a links) Complications: expandable to a lattice, with non-exclusive daughter nodes Inheritable features and relations (now looks more like a dictionary) “Instances” can hang from bottom nodes (providing grounding)

Semantic lexicon Provides a syntactic context for the appearance of the lexical item Provides a mapping for the lexical item to a node in the ontology Or more complex associations Also providing connections from syntactic context to semantic roles And constraints on these roles

Deriving basic semantic dependency (a toy example) Input: John makes tools Syntactic Analysis: cat verb tense present subject   root john cat noun-proper object   root     tool cat noun number plural

Relevant parts of the (appropriate sense of the lexical entry for make) make-v1 syn-struc root make cat v subj root $var1 cat n object root $var2 cat n sem-struc manufacturing-activity agent ^$var1 theme ^$var2

Relevant Extract from the Specification of the Ontological Concept Used to Describe the Appropriate Meaning of make: manufacturing-activity ... agent human theme artifact …

Relevant parts of the (appropriate senses of the) lexicon entries for John and tool John-n1 syn-struc root john cat noun-proper sem-struc human name john gender male tool-n1 syn-struc root tool cat n sem-struc tool

The basic semantic dependency component of the TMR for John makes tools is as follows: … manufacturing-activity-7 agent human-3 theme set-1 element tool cardinality > 1

try-v3 syn-struc root try cat v subj root $var1 cat n xcomp root $var2 form OR infinitive gerund sem-struc set-1 element-type refsem-1 cardinality >=1 refsem-1 sem event agent ^$var1 effect refsem-2 modality modality-type epiteuctic modality-scope refsem-2 modality-value < 1 refsem-2 value ^$var2 sem event

Constructing an IL representation For each syntactic analysis Access all semantic mappings and contexts for each lexical item Create all possible semantic representations Test them for coherency of structure and content

“Why is Iraq developing weapons of mass destruction?”

Concluding question Is all this really necessary? Do we need it to do – Machine Translation, IR, IE, Q/A, summarization? Can we “ground” the symbols of language without a special representation of the “meaning”?

Word sense disambiguation Constraint checking – making sure the constraints imposed on context are met Graph traversal – is-a links are inexpensive Other links are more expensive The “cheapest” structure is the most coherent Hunter-gatherer processing