Leon Bergen
Biographic Data
| ID | 3842864 |
|---|---|
| NAME | Leon Bergen |
| GIVEN NAMES | Leon |
| FAMILY NAME | Bergen |
| SIGNATURE | BERGEN L |
| AFFILIATIONS | Departments of aBrain and Cognitive Sciences and |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 2 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2013 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 1 |
Theory-driven statistical modeling for semantics and pragmatics: A case study on grammatically generated implicature readings
Computational probabilistic modeling is increasingly popular in linguistics, but its relationship with linguistic theory is ambivalent. We argue here for the potential benefit of theory-driven statistical modeling, based on a case study situated at the semantics-pragmatics interface. Using data from a novel experiment, we employ Bayesian model comparison to evaluate the predictive adequacy of four models that differ in the extent to and manner in…
How Efficiency Shapes Human Language
Pragmatic reasoning through semantic inference
A number of recent proposals have used techniques from game theory and Bayesian cognitive science to formalize Gricean pragmatic reasoning (Frank & Goodman, 2012; Franke, 2009; Goodman & Stuhlmuller, 2013; Jager, 2012). We discuss two phenomena which pose a challenge to these accounts of pragmatics: M-implicatures (Horn, 1984) and embedded implicatures which violate Hurford’s constraint (Chierchia, Fox, & Spector, 2012; Hurford, 1974). While tech…
Rational integration of noisy evidence and prior semantic expectations in sentence interpretation
Sentence processing theories typically assume that the input to our language processing mechanisms is an error-free sequence of words. However, this assumption is an oversimplification because noise is present in typical language use (for instance, due to a noisy environment, producer errors, or perceiver errors). A complete theory of human sentence comprehension therefore needs to explain how humans understand language given imperfect input. Ind…
Theory-driven statistical modeling for semantics and pragmatics: A case study on grammatically generated implicature readings
Computational probabilistic modeling is increasingly popular in linguistics, but its relationship with linguistic theory is ambivalent. We argue here for the potential benefit of theory-driven statistical modeling, based on a case study situated at the semantics-pragmatics interface. Using data from a novel experiment, we employ Bayesian model comparison to evaluate the predictive adequacy of four models that differ in the extent to and manner in…
Rational integration of noisy evidence and prior semantic expectations in sentence interpretation
Sentence processing theories typically assume that the input to our language processing mechanisms is an error-free sequence of words. However, this assumption is an oversimplification because noise is present in typical language use (for instance, due to a noisy environment, producer errors, or perceiver errors). A complete theory of human sentence comprehension therefore needs to explain how humans understand language given imperfect input. Ind…
Pragmatic reasoning through semantic inference
A number of recent proposals have used techniques from game theory and Bayesian cognitive science to formalize Gricean pragmatic reasoning (Frank & Goodman, 2012; Franke, 2009; Goodman & Stuhlmuller, 2013; Jager, 2012). We discuss two phenomena which pose a challenge to these accounts of pragmatics: M-implicatures (Horn, 1984) and embedded implicatures which violate Hurford’s constraint (Chierchia, Fox, & Spector, 2012; Hurford, 1974). While tech…
How Efficiency Shapes Human Language
Theory-driven statistical modeling for semantics and pragmatics: A case study on grammatically generated implicature readings
Computational probabilistic modeling is increasingly popular in linguistics, but its relationship with linguistic theory is ambivalent. We argue here for the potential benefit of theory-driven statistical modeling, based on a case study situated at the semantics-pragmatics interface. Using data from a novel experiment, we employ Bayesian model comparison to evaluate the predictive adequacy of four models that differ in the extent to and manner in…
Computer Science (4 works) · Linguistics (4 works) · Natural Language Processing Techniques (4 works) · Artificial Intelligence (3 works) · Inference (3 works) · Natural language processing (3 works) · Utterance (3 works) · Artificial Intelligence (2 works) · Implicature (2 works) · Interpretation (philosophy) (2 works)