Aleksei Nazarov
Biographic Data
| ID | 9041394 |
|---|---|
| NAME | Aleksei Nazarov |
| GIVEN NAMES | Aleksei |
| FAMILY NAME | Nazarov |
| SIGNATURE | NAZAROV A |
| VERIFIED | No |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2016 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Generalizing French Schwa Deletion
Indexed constraints (like cophonologies) increase a grammar’s fit to seen data, but do they hurt the grammar’s ability to generalize to unseen data? We focus on French schwa deletion, an optional process whose rate of application is modulated by both phonological and lexical factors, and we propose three indexed constraint learners in the Maximum Entropy (MaxEnt) framework. Using data from Racine (2008), we test the ability of four learners to ca…
Learning within- and between-word variation in probabilistic OT grammars
This paper proposes a novel method of inferring diacritics for representing between-word variation (exceptionality) in Optimality Theoretic (OT) grammars (e.g., Pater 2000, 2010) that makes it possible to infer such diacritics in the face of within-word variation. Existing methods of inferring diacritics in OT (Pater 2010, Becker 2009, Coetzee 2009) are based in categorical grammar learning (Tesar 1995), which makes them unable to handle within-w…
Learning opacity in Stratal Maximum Entropy Grammar
Opaque phonological patterns are sometimes claimed to be difficult to learn; specific hypotheses have been advanced about the relative difficulty of particular kinds of opaque processes (Kiparsky 1971, 1973), and the kind of data that is helpful in learning an opaque pattern (Kiparsky 2000). In this paper, we present a computationally implemented learning theory for one grammatical theory of opacity, a Maximum Entropy version of Stratal OT (Bermú…
Learning Parametric Stress without Domain-Specific Mechanisms
State-of-the-art learning mechanisms for stress in Optimality Theory (see, e.g., Tesar and Smolensky 2000; Boersma and Pater 2016; Jarosz 2013) make use of probabilistic mechanisms that are domain-general in that they do not refer to the content of constraints and must not be in UG. By contrast, Pearl (2007, 2011) has argued that domain-general probabilistic learners of parametric grammars (Yang 2002) are insufficient for word stress, and, instea…
Dutch Reduction Domains
[Abstract not available]
No prominent works on this page.
Dutch Reduction Domains
[Abstract not available]
Learning opacity in Stratal Maximum Entropy Grammar
Opaque phonological patterns are sometimes claimed to be difficult to learn; specific hypotheses have been advanced about the relative difficulty of particular kinds of opaque processes (Kiparsky 1971, 1973), and the kind of data that is helpful in learning an opaque pattern (Kiparsky 2000). In this paper, we present a computationally implemented learning theory for one grammatical theory of opacity, a Maximum Entropy version of Stratal OT (Bermú…
Learning Parametric Stress without Domain-Specific Mechanisms
State-of-the-art learning mechanisms for stress in Optimality Theory (see, e.g., Tesar and Smolensky 2000; Boersma and Pater 2016; Jarosz 2013) make use of probabilistic mechanisms that are domain-general in that they do not refer to the content of constraints and must not be in UG. By contrast, Pearl (2007, 2011) has argued that domain-general probabilistic learners of parametric grammars (Yang 2002) are insufficient for word stress, and, instea…
Learning within- and between-word variation in probabilistic OT grammars
This paper proposes a novel method of inferring diacritics for representing between-word variation (exceptionality) in Optimality Theoretic (OT) grammars (e.g., Pater 2000, 2010) that makes it possible to infer such diacritics in the face of within-word variation. Existing methods of inferring diacritics in OT (Pater 2010, Becker 2009, Coetzee 2009) are based in categorical grammar learning (Tesar 1995), which makes them unable to handle within-w…
Generalizing French Schwa Deletion
Indexed constraints (like cophonologies) increase a grammar’s fit to seen data, but do they hurt the grammar’s ability to generalize to unseen data? We focus on French schwa deletion, an optional process whose rate of application is modulated by both phonological and lexical factors, and we propose three indexed constraint learners in the Maximum Entropy (MaxEnt) framework. Using data from Racine (2008), we test the ability of four learners to ca…
Artificial Intelligence (5 works) · Computer Science (5 works) · Linguistics (5 works) · Natural language processing (4 works) · Natural Language Processing Techniques (4 works) · Linguistic Variation and Morphology (3 works) · Mathematics (3 works) · Philosophy (3 works) · Speech recognition (3 works) · Grammar (2 works)