Compatto e maneggevole, il dizionario della letteratura americana curato da Luca Briasco e Mattia Carratello si avvale del contributo di ventisette collaboratori e quattro coordinatori generali a ...sovrintendere i trecento lemmi relativi a narrativa (Briasco e Carratello), poesia (Paola Loreto) e teatro (Ruggero Bianchi). I due curatori, entrambi americanisti, lavorano per case editrici (Einaudi Stile libero e Sellerio), e ciò ha probabilmente influenzato il criterio di selezione adottato per un'opera che si dimostra molto attenta alle fluttuazioni del mercato editoriale, oltre che alle tendenze all'interno dell'accademia. Si tratta di un felice connubio tra divulgazione e competenza/rigore scientifici, che poi sono quelli dei tanti collaboratori che hanno lavorato al progetto.
The article addresses the use of dictionaries among the new millennium generation of English as a foreign language (EFL) undergraduates. Applying the mixed-method approach (a questionnaire and ...interviews), the study examines the frequency of dictionary use, the types of dictionaries used, activities initiating dictionary consultation, information searched for, and problems faced in using dictionaries. The findings suggest that the participants are most fond of bilingual online dictionaries and use them mostly for looking up the meaning of unknown words. They also show that despite being high consumers of technology, participants do not benefit much from online dictionaries, as they neglect most of the entry information. The qualitative data reveal that the participants perceive various digital tools of questionable quality as online dictionaries. Overall, the study sheds light on the characteristic behavior of the new generation of EFL learners regarding their dictionary use and points to the necessity of developing their digital competence in the realm of dictionary use.
Abstract
The study was an experimental endeavor to explore the use of an online bilingual dictionary in EFL writing by gauging its effect on writing scores, assessing its impact on lexical ...sophistication, and examining learners’ dictionary lookup behavior during writing. A class of EFL students (n=34) at a Chinese university were asked to write two English compositions, one without access to a dictionary and the other with the help of Bing.dict, one of the most popular online bilingual dictionaries in China. A screen recorder was installed to capture in real time the whole process of dictionary consultation and writing. Results indicate that the use of Bing.dict exerted a significantly negative effect on the overall composition scores and the component scores for content and language use, although it did help to increase the students’ lexical range as measured by the Lexical Frequency Profile. A variety of dictionary-based errors were committed in terms of lexicon, syntax and collocation due to the students’ inadequate dictionary use skills and the unsatisfactory quality of the dictionary for productive language use. A close examination of the screen recordings reveals that the students adopted a range of poor consultation strategies during writing. They were content to find an English equivalent to a Chinese lexical item without bothering to further check its meaning and usage. In addition, they tried in vain to find ready text translations for Chinese sentences or sentence fragments. Focusing on the unreliable web-crawled examples, very few of the students paid attention to the source dictionaries. Besides, most of them were prone to accept what was offered in the dictionary without independent thinking. The findings of the study show a rather disappointing picture of the use of an online bilingual dictionary for language production, suggesting that it is absolutely imperative to train EFL learners on dictionary use.
A novel dictionary learning algorithm, namely reconstructive and discriminative dictionary learning based on sparse representation classification criterion (RDDLSRCC), is proposed for radar target ...high resolution range profile (HRRP) recognition in this paper. The core of proposed algorithm is to incorporate the reconstructive power and discriminative power of atoms during the update of atoms. By constructing the objective function based on sparse representation classification criterion (SRCC), the discriminative performance of atoms can be improved while preserving the same-class reconstruction ability of atoms and reducing their reconstruction contribution to other classes. Moreover, the sparse coding coefficients of samples are updated using class-optimal SVD vectors of class-reconstruction residual matrix, thereby accelerating convergence. Compared with other dictionary learning algorithms, RDDLSRCC is more robust to the variation of target aspect and noise׳s effect. The extensive experimental results on the measured data illustrate that the proposed algorithm achieves a promising target recognition performance.
•The reconstructive and discriminative power of atom is considered during the update.•We construct an objective function based on SRC criterion.•Sparse coding coefficients of samples are updated using class-optimal SVD vectors.•RDDLSRCC is more robust to the variation of target aspect and noise׳ effect.
Abstract
Interviews with undergraduate students from the University of Ljubljana, who are majoring in English and can be considered language specialists, investigated habits of dictionary use, ...look-up abilities, and perceptions of the utility and quality of definitions and illustrative examples. This contrasts with a parallel study (Farina et al. 2019) with undergraduates majoring in business and economics. Like the parallel study, this study was based on fourteen questions and nine contexts containing a clearly-marked common word used in an infrequent sense; participants had to locate the sense in a dictionary that, at the time of the studies, was the online Merriam–Webster Learner’s Dictionary, rebranded today as The Britannica Dictionary. Participants were asked to think aloud as they looked up words. Among other results, the study revealed that its participants, while they were linguistically-educated and experienced, did not fully grasp the complexity of presenting dictionary information online.
•EDL optimally assign different number of dictionary items to each class.•EDL learns different number of dictionary items for different classes.•Number of sharing/discriminative dictionary items is ...set optimally by EDL.•Proposed method (EDL) learns correct number of shared/discriminative items.
In this paper, a new discriminative dictionary learning algorithm is introduced. An entropy based criterion is embedded into the objective function to enforce a proper structure for the dictionary items when decomposing signals of different classes. The proposed criterion influences the dictionary items to participate in the decomposition of a smaller number of classes as possible. Unlike the other methods, columns of the dictionary are not restricted to have pre-assigned labels and they are free to be representative of any class or to share features of several classes. The number of shared and discriminative items along with the number of dictionary items for each specific class is learned dynamically during the optimization process, depending on the complexity of the classification task and the distribution of different classes. The experimental results demonstrate that the proposed entropy based dictionary learning (EDL) algorithm outperforms other discriminative dictionary learning methods using several real-world image datasets.
In this study, an inverse synthetic aperture radar (ISAR) image resolution enhancement algorithm based on joint dictionary learning is proposed, by which two special sets of sparse signals called ...dictionaries are solved by exploiting numerous high-resolution (HR) and low-resolution (LR) ISAR images. Herein a new coupled dictionary learning algorithm based on restricted Boltzmann machine (RBM) is designed to learn a LR and a HR dictionary using LR and HR image patches. Since the echoes are equivalent to similar scattering-centre models when an object is illuminated by radar signals with same centre frequency and different bandwidth, respectively, it is reasonable to assume the object's LR ISAR image shares the same sparse representation coefficients with its HR ISAR image. When a LR ISAR image is represented sparsely with a LR dictionary, a HR ISAR images can be reconstructed based on a HR dictionary owing to the similar sparse representation coefficients. Experiment results with simulation data demonstrate the superior performance of the proposed method over other classical dictionary training algorithms.
Recently, image de-noising algorithm based on sparse representation has received an increasing amount of attention. Such algorithms proposed a comprehensive sparse representation model, by solving ...the sparse coding problem and choosing the proper method for dictionary updating to achieve better de-noising results. Therefore, the construction of learning dictionary has become one of the key problems that limit the de-noising effectiveness. The non-locally centralised sparse representation de-noising algorithm uses principal component analysis method to achieve dictionary updating. Nevertheless, the instability of a single complete dictionary in sparse coding leads to erratic result in the process of the original image restoration. In this study, the authors present a new method named generalised non-locally centralised sparse representation algorithm. In the proposed method, the authors cluster the training patches extracted from a set of example images into subspaces, and then train dictionaries for subspaces by sparse analysis k-singular value decomposition dictionary, which is utilised to construct coded sub-block dictionary to avoid the instable results caused by a single dictionary. Experiments show that the improved method has better signal-to-noise ratio and de-noising effect compared with other methods.
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