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1.
Yat-alternation and the imperfect tense in Bulgarian. A rule-based analysis.
Danil Khristov, 2022, published scientific conference contribution

Abstract: 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.
Keywords: yat vowel, yat-alternation, variable ya/e, imperfect tense, rule-based analysis, features
Published in RUNG: 06.09.2022; Views: 1199; Downloads: 0
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2.
Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms
Nada Lavrač, Johannes Fuernkranz, Dragan Gamberger, 2010, independent scientific component part or a chapter in a monograph

Abstract: 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.
Keywords: Machine learning, Feature construction, Rule learning, Unknown attribute values
Published in RUNG: 14.07.2017; Views: 4264; Downloads: 0
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