Objectives: Word definition skill is a complex language ability in which meta-linguistic awareness and literacy skills play a critical role. The present study examined the development of word definition skills in Persian-speaking children aged 4.5 to 7.5 years, concerning content and form aspects. Methods: This was a cross-sectional and analytic-descriptive study. The study subjects were 107 c...

A new word-clustering technique is proposed to efficiently build statistically salient class 2-grams from language corpora. By splitting word neighboring characteristics into word-preceding and following directions, multiple (two-dimensional) word classes are assigned to each word. In each side, word classes are merged into larger clusters independently according to preceding or following word ...

A novel approach is presented to class-based language modeling based on part-of-speech statistics. It uses a deterministic word-to-class mapping, which handles words with alternative part-of-speech assignments through the use of ambiguity classes. The predictive power of word-based language models and the generalization capability of class-based language models are combined using both linear in...

In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem for spoken language in that it is difficult to collect training data. The Multi-Class Composite N-gram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters, called MultiClasses. In the Multi-Cl...

The purpose of this study was twofold: (a) to assess the retention of two word types (synonyms and homonyms) in the short term memory, and (b) to investigate the effect of these word types on word learning by asking learners to learn their Persian meanings. A total of 73 Iranian language learners studying English translation participated in the study. For the first purpose, 36 freshmen from an ...

The word class effect in the picture-word interference paradigm is a highly influential finding that has provided some of the most compelling support for word class constraints on lexical selection. However, methodological concerns called for a replication of the most convincing of those effects. Experiment 1 was a direct replication of Pechmann and Zerbst (2002; Experiment 4). Participants nam...

We address the issue of data sparseness problem in language model (LM). Using class LM is one way to avoid this problem. In class LM, infrequent words are supported by more frequent words in the same class. This paper investigates a class LM based on LSA. A word-document matrix is usually used to represent a corpus in LSA framework. However, this matrix ignores word order in the sentence. We pr...

In this paper, a new language model, the Multi-Class Composite N-gram, is proposed to avoid a data sparseness problem in small amount of training data. The Multi-Class Composite Ngram maintains an accurate word prediction capability and reliability for sparse data with a compact model size based on multiple word clusters, so-called Multi-Classes. In the Multi-Class, the statistical connectivity...