Ossetian: Revisiting Inflectional Morphology

Ossetian, a language of the Northeastern group of the Indo-Iranian branch of the Indo-European stock of languages, has not received as much linguistic attention as it deserves. A few major studies on Ossetian were written in the 19th and 20th centuries, most of them in Russian. While these works are a solid foundation in the study of Ossetian, its description is not complete. The present work, written in English, offers Ossetian to a wider international audience. Relying on new developments in linguistic theory, it reexamines phenomena in the inflectional morphology of Ossetian. The preliminary chapter on phonology provides an overview of the phonemic inventory of Ossetian. In the chapter on nominal morphology, the variety and nature of case and number suffixes is re-analyzed and they are described as phrasal affixes. In the chapter on verbal morphology, the forms previously described as infinitives are discussed and one of them is reanalyzed as a derived noun or adjective; the majority of verbs are regarded as having one stem form; tense is analyzed as a suffix that attaches to the stem; mood, person, number and transitivity marking is analyzed as one fused suffix

Morfologi Infleksional dan Derivasional dalam Proses Morfologi Bahasa Indonesia

The purpose of this research is to explain derivational and inflectional morphology, that is to explain a.ffu:es and formed words in Bahasa Indonesia that belongs to inflectional and derricational morphology.The data used in this study are linguistic data ; that is the use of spoken or written language. Keywords : inflectional morphology, derivational morpholog

Morphologically Aware Word-Level Translation

We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way. Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectional morphology provides additional syntactic information. This approach leads to substantial performance improvements - 19% average improvement in accuracy across 6 language pairs over the state of the art in the supervised setting and 16% in the weakly supervised setting. As another contribution, we highlight issues associated with modern BLI that stem from ignoring inflectional morphology, and propose three suggestions for improving the task.Comment: COLING 202

One-Shot Neural Cross-Lingual Transfer for Paradigm Completion

We present a novel cross-lingual transfer method for paradigm completion, the task of mapping a lemma to its inflected forms, using a neural encoder-decoder model, the state of the art for the monolingual task. We use labeled data from a high-resource language to increase performance on a low-resource language. In experiments on 21 language pairs from four different language families, we obtain up to 58% higher accuracy than without transfer and show that even zero-shot and one-shot learning are possible. We further find that the degree of language relatedness strongly influences the ability to transfer morphological knowledge.Comment: Accepted at ACL 201

Embedded Aspect in L2 Acquisition: Evidence from L1 Russian Learners of Greek.

This work investigates first language (L1) influence on the second language (L2) acquisition of aspect, comparing participants with homogeneous L1 background (Russian) in Mainland Greece (L2 Standard Modern Greek) and Cyprus (L2 Cypriot Greek), where verb complementation takes a finite form instead of an infinitival as is possible in Russian. Focus of the experimental study lies on embedded environments, which require only perfective aspect in Greek but allow either perfective or imperfective in Russian. The findings support the Full Transfer/Full Access Hypothesis, according to which aspect is part of Universal Grammar and L2 learners can reach native�‑like attainment due to access to it, while at the initial stage of L2 acquisition transfer from L1 into L2 takes place

The Perfective Past Tense in Greek Adolescents with Down Syndrome

This study investigates the ability of a group of eight Greek-speaking adolescents with Down Syndrome (DS) (aged 12.1-18.7) to handle the perfective past tense using an acceptability judgement task. The performance of the DS participants was compared with that of 16 typically-developing children whose chronological age was matched with the mental age of the DS group. For existing verbs, both groups showed high accuracy scores for the sigmatic past tense whilst for (potential but non-existing) nonce verbs the DS group performed differently from the controls. Specifically, their judgements were unaffected by a nonce verb's similarity to existing verbs, unlike those of the controls, suggesting that the DS participants were less reliant on similarity-based generalisations when encountering a nonce word than the controls. Apart from that, it was found that people with DS did not show any kind of morphological impairment, replicating previous findings on past tense production in DS

Plural morphology in compounding is not good evidence to support the dual mechanism model

The compounding phenomena is considered to be good evidence to support the dual mechanism model of morphological processing (Pinker & Prince, 1992). However evidence from initial neural net modeling has shown that a single route associative memory based account might provide an equally, if not more valid explanation of the treatment of plurals in compounds. Further neural net modeling and empirical work is proposed to test this single route accoun

Development of a Hindi Lemmatizer

We live in a translingual society, in order to communicate with people from different parts of the world we need to have an expertise in their respective languages. Learning all these languages is not at all possible; therefore we need a mechanism which can do this task for us. Machine translators have emerged as a tool which can perform this task. In order to develop a machine translator we need to develop several different rules. The very first module that comes in machine translation pipeline is morphological analysis. Stemming and lemmatization comes under morphological analysis. In this paper we have created a lemmatizer which generates rules for removing the affixes along with the addition of rules for creating a proper root word