1. The interface between model-theoretic and corpus-based semantics
Sebastian Pado

2. Natural language semantics
Model-theoretic semantics
Compositional calculation of sentence meaning
Formal descriptions of ambiguities
Inference
Corpus-based semantics
Distributional, graded meaning representation
Probabilistic knowledge acquisition from corpora
Prediction of linguistic behaviour based on context
Anbindung an andere Gebiete:
Modelltheoretische Semantik: formale Linguistik (insbes. Grammatikformalismen), Logik, theoretische Informatik;
Korpusbasierte Semantik: kognitive Psychologie, Maschinelles Lernen

3. Complementary benefits
Corpus-based
semantics
Good for lexical level
(open word classes)
High coverage, robustness
Approximative
Model-theoretic
semantics
Good for sentence level
(closed word classes)
Limited coverage
Correct
Kriterien für Erfolg: ich persönlich denke an eine anwendungs (NLP)-bezogene Evaluation; bin aber für weitere Vorschläge sehr offen
How to divide work between the approaches?

4. Strategies
More expressive representations for corpus-based models of meaning: Compositionality in vector spaces
Ongoing collaboration with Katrin Erk (Dept. of Linguistics, U. Texas at Austin)
Corpus-based methods for enrichment of formal meaning representations
Core of SFB project proposal
Spezifikation durch Kontext

5. Strategy 1
More expressive representations for corpus-based models of meaning

6. Compositionality in Vector Spaces
Vector space: Representation of word meaning by context co-occurrences
What is the representation of a phrase?
Centroid of two vectors?
No: Must take mode of combination into account
“a horse draws…” : pull
“draw a horse” : sketch

7. A first step Structured vector space model [Erk & Pado 2008]
Covers Verb+Object, Verb+Subject combinations
Word meaning consists of lexical vector plus selectional preferences (=experiences) for dependents/governors
Note: selectional preference representation is exemplar-based

8. A first step Structured vector space model [Erk & Pado 2008]
Covers Verb+Object, Verb+Subject combinations
Phrase meaning consists of two vectors:
Verb meaning modified by nominal expectations about governor
Noun meaning modified by verbal expectations about dependent

9. Current state
Evaluation: Better distinction between contextually appropriate and inappropriate paraphrases (WSD-style task)
Further research questions
Generalisation to longer phrases
More expressive model of expectations
Modelling of phrases involving closed word classes
E.g. Negation

10. Strategy 2
Corpus-based methods for enrichment of formal meaning representations

11. Formal models of meaning in context
Lexicon entries cannot provide the full range of readings for words/phrases
Readings often productively negotiated in text
Type/sort conflict
Examples:
Metonymy/Metaphor
Telic adjectives (“fast typist”)
Coercion/Reinterpretation

12. Example: Coercion
Wegen einer 15-jährigen kam es zu einem Streit, in dessen Verlauf sie verletzt wurde.
[…] Sie hatte sich mit einem 21-jährigen unterhalten.
Red and blue expressions are coreferring, but red expression has wrong type (wegen takes <e,t>; expression is <e>).
Here, context overtly provides missing event
Often, this is not the case: Operator must be recovered from general knowledge

13. The role of corpus methods
Acquisition of general reinterpretation operators from corpora
Recovery/prediction of operators for instances with type/sort conflict
Making implicit meaning explicit: can be seen as context-driven semantic specification
Interest primarily empirical

14. Project Steps
Creation of multilingual corpus of type/sort conflict cases with human annotations
Informed by formal considerations
Development of CL methods to predict operators for conflict resolution
Ideally, task-based evaluation (to be determined)
Consequences/insights for formal descriptions

15. Research Questions
When can operators be found overtly in context; when must general operators be recovered?
Influence of local discourse?
CL methods for efficient and accurate prediction of operators
What linguistic levels are helpful? Semantic classes, semantic roles, dependency relations, …?
Focus on more than one language: Can bilingual processing help?
What is the level of generality of acquired operators?
What shape do people’s expectations have?
Do peoples’ judgments of recovered operators agree?
Can empirical results have impact on formal descriptions?
E.g. do sort and type conflicts behave differently or similarly?
Relation to work on textual entailment?

16. Collaborations D1 (Representation of ambiguities)
Formal descriptions as information source for corpus development
Attempt to transfer of empirical results back into theory
B5 (Polysemy in a conceptual system)
Ontological information as knowledge source for CL operator models
Entailment as shared evaluation task
Open for other ideas