Applying phraseological complexity measures to L2 French

This study partially replicates Paquot’s (2018, 2019) study of phraseological complexity in L2 English by investigating how phraseological complexity compares across proficiency levels as well as how phraseological complexity measures relate to lexical, syntactic and morphological complexity measures in a corpus of L2 French argumentative essays. Phraseological complexity is operationalized as the diversity (root type-token ratio; RTTR) and sophistication (pointwise mutual information; PMI) of three types of grammatical dependencies: adjectival modifiers, adverbial modifiers and direct objects. Results reveal a significant increase in the mean PMI of direct objects and the RTTR of adjectival modifiers across proficiency levels. In addition to phraseological sophistication, important predictors of proficiency include measures of lexical diversity, lexical sophistication, syntactic (phrasal) complexity and morphological complexity. The results provide cross-linguistic validation for the results of Paquot (2018, 2019) and further highlight the importance of including phraseological measures in the current repertoire of L2 complexity measures.

The Drama in the Sentence: Sequence as a Crucial Challenge for Literary Translation from and to Korean

In translation, carefully-crafted sentences are exposed to myriad dangers. This is because translators tend to prioritize syntactical fidelity at the expense of sequence, that is, the order of elements insofar as this relates to calculated progression, gradual disclosure of information, and cumulative development of meaning. But if sequence is turned around for the sake of fluency (conforming to the target language’s ostensibly “natural” word order), the reader’s experience changes as well. Through a set of examples drawn from English translations of Korean fiction, this article demonstrates that the common disregard for sequence is tantamount to a neglect of drama and suspense, of narrative perspectivation, of rhetorical sophistication and cognitive effect. But we also see that by favoring functional equivalence over imitation of grammatical dependencies, it is perfectly possible to allow the reader to process all information at a pace that is analogous to that of the original. Our findings provide insights that are of significance for other language pairings as well.

Translating Collocations from English into Arabic and vice versa: An Empirical Study

Collocations are defined as ''the frequent co-occurrence of lexical items that naturally share the characteristics of semantic and grammatical dependencies"(Ibrahim, 2003: iii). In translation, collocations are considered a factor that makes translation more effective and powerful. However, translating collocations is an everlasting struggle for most students of translation. The present study aims at investigating the challenges that Sudanese EFL university students encounter when rendering English collocations into their Arabic equivalences and vice versa as well as the reasons behind these challenges. To this end, 26 Sudanese EFL students, between 20-30 years old, studying at Nahda College in Sudan, were selected. A diagnostic test composed of two questions is used as a tool for data collection. Frequencies, percentages, mean, and standard deviation are used to analyse the collected data. The results of this study manifests that Sudanese EFL university students encounter difficulties in translating collocations from English into Arabic and vice versa; the causes of these difficulties are due to students’ unawareness of the linguistic and cultural differences between the two languages as well as their heavy reliance on literal translation strategy.

A 3-phase approach based on sequential mining and dependency parsing for enhancing hypernym patterns performance

Patterns have been extensively used to extract hypernym relations from texts. The most popular patterns are Hearst’s patterns, formulated as regular expressions mainly based on lexical information. Experiences have reported good precision and low recall for such patterns. Thus, several approaches have been developed for improving recall. While these approaches perform better in terms of recall, it remains quite difficult to further increase recall without degrading precision. In this paper, we propose a novel 3-phase approach based on sequential pattern mining to improve pattern-based approaches in terms of both precision and recall by (i) using a rich pattern representation based on grammatical dependencies (ii) discovering new hypernym patterns, and (iii) extending hypernym patterns with anti-hypernym patterns to prune wrong extracted hypernym relations. The results obtained by performing experiments on three corpora confirm that using our approach, we are able to learn sequential patterns and combine them to outperform existing hypernym patterns in terms of precision and recall. The comparison to unsupervised distributional baselines for hypernym detection shows that, as expected, our approach yields much better performance. When compared to supervised distributional baselines for hypernym detection, our approach can be shown to be complementary and much less loosely coupled with training datasets and corpora.

An enhanced lexicon-based approach for sentiment analysis: a case study on illegal immigration

PurposeResearch on sentiment analysis were mostly conducted on product and services, resulting in scarcity of studies focusing on social issues, which may require different mechanisms due to the nature of the issue itself. This paper aims to address this gap by developing an enhanced lexicon-based approach.Design/methodology/approachAn enhanced lexicon-based approach was employed using General Inquirer, incorporated with multi-level grammatical dependencies and the role of verb. Data on illegal immigration were gathered from Twitter for a period of three months, resulting in 694,141 tweets. Of these, 2,500 tweets were segregated into two datasets for evaluation purposes after filtering and pre-processing.FindingsThe enhanced approach outperformed ten online sentiment analysis tools with an overall accuracy of 81.4 and 82.3% for dataset 1 and 2, respectively as opposed to ten other sentiment analysis tools.Originality/valueThe study is novel in the sense that data pertaining to a social issue were used instead of products and services, which require different mechanism due to the nature of the issue itself.

Production Practice During Language Learning Improves Comprehension

Language learners often spend more time comprehending than producing a new language. However, memory research suggests reasons to suspect that production practice might provide a stronger learning experience than comprehension practice. We tested the benefits of production during language learning and the degree to which this learning transfers to comprehension skill. We taught participants an artificial language containing multiple linguistic dependencies. Participants were randomly assigned to either a production- or a comprehension-learning condition, with conditions designed to balance attention demands and other known production–comprehension differences. After training, production-learning participants outperformed comprehension-learning participants on vocabulary comprehension and on comprehension tests of grammatical dependencies, even when we controlled for individual differences in vocabulary learning. This result shows that producing a language during learning can improve subsequent comprehension, which has implications for theories of memory and learning, language representations, and educational practices.

Human Information Processing Shapes Language Change

Human languages exhibit both striking diversity and abstract commonalities. Whether these commonalities are shaped by potentially universal principles of human information processing has been of central interest in the language and psychological sciences. Research has identified one such abstract property in the domain of word order: Although sentence word-order preferences vary across languages, the superficially different orders result in short grammatical dependencies between words. Because dependencies are easier to process when they are short rather than long, these findings raise the possibility that languages are shaped by biases of human information processing. In the current study, we directly tested the hypothesized causal link. We found that learners exposed to novel miniature artificial languages that had unnecessarily long dependencies did not follow the surface preference of their native language but rather systematically restructured the input to reduce dependency lengths. These results provide direct evidence for a causal link between processing preferences in individual speakers and patterns in linguistic diversity.