Automatic Error Detection concerning the Definite and Indefinite Conjugation in the HunLearner Corpus
In this paper we present the results of automatic error detection, concerning the definite and indefinite conjugation in the extended version of the HunLearner corpus, the learners corpus of the Hungarian language. We present the most typical structures that trigger definite or indefinite conjugation in Hungarian and we also discuss the most frequent types of errors made by language learners in the corpus texts. We also illustrate the error types with sentences taken from the corpus. Our results highlight grammatical structures that might pose problems for learners of Hungarian, which can be fruitfully applied in the teaching and practicing of such constructions from the language teachers or learners point of view. On the other hand, these results may be exploited in extending the functionalities of a grammar checker, concerning the definiteness of the verb. Our automatic system was able to achieve perfect recall, i.e. it could find all the mismatches between the type of the object and the conjugation of the verb, which is promising for future studies in this area.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Say Anything: Automatic Semantic Infelicity Detection in L2 English Indefinite Pronouns
Computational research on error detection in second language speakers has mainly addressed clear grammatical anomalies typical to learners at the beginner-to-intermediate level. We focus instead on acquisition of subtle …
Epistemic Indefinites and Reportative Indefinites in Cantonese
Towards Unbiased Random Features with Lower Variance For Stationary Indefinite Kernels
Random Fourier Features (RFF) demonstrate wellappreciated performance in kernel approximation for largescale situations but restrict kernels to be stationary and positive definite. And for non-stationary kernels, the cor…
regressionAnalysis of SVM with Indefinite Kernels
The recent introduction of indefinite SVM by Luss and dAspremont [15] has effectively demonstrated SVM classification with a non-positive semi-definite kernel (indefinite kernel). This paper studies the properties of t…
Building a Corpus of Indefinite Uses Annotated with Fine-grained Semantic Functions
Natural languages possess a wealth of indefinite forms that typically differ in distribution and interpretation. Although formal semanticists have strived to develop precise meaning representations for different indefini…