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3. Forms of address, performative prefixes, and the syntax-pragmatics interfaceTue Trinh, 2024, izvirni znanstveni članek Opis: Forms of address must be pronominal in English but can be either pronominal or nominal in Vietnamese. I propose to analyze this fact as a parametric difference: the two languages choose different ways to implement one and the same general preference principle. This principle is Rule I, which favors binding over coreference. For English, Rule I compares bound and free expressions. For Vietnamese, Rule I compares bound and free pronouns. The analysis crucially relies on the hypothesis that speech acts are represented in the syntax.
Ključne besede: performative hypothesis, binding theory, rule I, pronouns
Objavljeno v RUNG: 08.01.2025; Ogledov: 283; Prenosov: 4
 Celotno besedilo (455,51 KB)
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4. Yat-alternation and the imperfect tense in Bulgarian. A rule-based analysis.Danil Khristov, 2022, objavljeni znanstveni prispevek na konferenci Opis: The paper proposes a rule-based feature analysis of the ya/e phenomenon in Bulgarian. Special attention
is paid to the variable ya/е observed in the forms of the imperfect tense. First and second-conjugation verbs whose
imperfect forms involve yat-alternation are compared with third-conjugation verbs where this alternation is not observed. The analysis also addresses the role of morphology in the process of adding different imperfect endings to
the verb base and the effect of these endings on the variable ya/e. Finally, the phonemic status of soft consonants is
discussed in relation to the proposed analysis.
Ključne besede: yat vowel, yat-alternation, variable ya/e, imperfect tense, rule-based analysis, features
Objavljeno v RUNG: 06.09.2022; Ogledov: 2895; Prenosov: 0
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5. Explicit Feature Construction and Manipulation for Covering Rule Learning AlgorithmsNada Lavrač, Johannes Fuernkranz, Dragan Gamberger, 2010, samostojni znanstveni sestavek ali poglavje v monografski publikaciji Opis: Features are the main rule building blocks for rule learning algorithms. They can be simple tests for attribute values or complex logical terms representing available domain knowledge. In contrast to common practice in classification rule learning, we argue that separation of the feature construction and rule construction processes has theoretical and practical justification. Explicit usage of features enables a unifying framework of both propositional and relational rule learning and we present and analyze procedures for feature construction in both types of domains. It is demonstrated that the presented procedure for constructing a set of simple features has the property that the resulting set enables construction of complete and consistent rules whenever it is possible, and that the set does not include obviously irrelevant features. Additionally, the concept of feature relevancy is important for the effectiveness of rule learning. It this work, we illustrate the concept in the coverage space and
prove that the relative relevancy has the quality-preserving property in respect to the resulting rules. Moreover, we show that the transformation from the attribute to the feature space enables a novel, theoretically justified way of handling unknown attribute values. The same approach
enables that estimated imprecision of continuous attributes can be taken into account, resulting in construction of robust features in respect to this imprecision.
Ključne besede: Machine learning, Feature construction, Rule learning, Unknown attribute values
Objavljeno v RUNG: 14.07.2017; Ogledov: 5306; Prenosov: 0
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