Connectionist approaches to word learning and word recognition imply that the units of lexical representation are not part of a fixed architecture. If the size of stored units is determined by extracting co-occurrence regularities, then we expect the lexicon to include multi-word units which have become entrenched due to repeated use. Experiments using Reicher's two-alternative forced-choice pr...

The article describes the study of models word-formation during teaching foreign languages. author pays special attention to formation minimum and German word stems. When selecting minimum, a large number micromodels lexical stems relevant modern vocabulary was identified, it found that identified there are about 250. At all stages learning German, both at school in higher school, we should rev...

Literary and dialect languages influence each other and, as a result, mutual enrichment occurs, in particular botanical vocabulary. Comparison of folk literary floristic names the Ukrainian language reveals: common for dialects (with phonetic, word-formative, semantic variants dialects); with different lexical meanings; many words broader structure dialects; figurative meaning existence lexemes...

The words of a language reflect the structure of the human mind, allowing us to transmit thoughts between individuals. However, language can represent only a subset of our rich and detailed cognitive architecture. Here, we ask what kinds of common knowledge (semantic memory) are captured by word meanings (lexical semantics). We examine a prominent computational model that represents words as ve...

The present experiments explored the role of processing level and strategic factors in cross-form (word-picture and picture-word) and within-form (picture-picture and word-word) semantic facilitation. Previous studies have produced mixed results. The findings presented in this article indicate that semantic facilitation depends on the task and on the subjects' strategies. When the task required...

Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word r...

This paper describes our participation in the message polarity classification task of SemEval 2014. We focused on exploiting unlabeled data to improve accuracy, combining features leveraging word representations with other, more common features, based on word tokens or lexicons. We analyse the contribution of the different features, concluding that unlabeled data yields significant improvements.

Although most studies of reading English (and other alphabetic languages) have indicated that readers do not obtain preview benefit from word n + 2, Yang, Wang, Xu, and Rayner (2009) reported evidence that Chinese readers obtain preview benefit from word n + 2. However, this effect may not be common in Chinese because the character prior to the target word in Yang et al.'s experiment was always...