Relevant bibliographies by topics / Word-Learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Word-Learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 March 2023

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Journal articles on the topic "Word-Learning"

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Bloom, Paul. "Word learning." Current Biology 11, no. 1 (January 2001): R5—R6. http://dx.doi.org/10.1016/s0960-9822(00)00032-4.

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Ryder, Dan, and Oleg V. Favorov. "Empiricist word learning." Behavioral and Brain Sciences 24, no. 6 (December 2001): 1117. http://dx.doi.org/10.1017/s0140525x01360132.

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At first, Bloom's theory appears inimical to empiricism, since he credits very young children with highly sophisticated cognitive resources (e.g., a theory of mind and a belief that real kinds have essences), and he also attacks the empiricist's favoured learning theory, namely, associationism. We suggest that, on the contrary, the empiricist can embrace much of what Bloom says.
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He, Angela Xiaoxue, and Sudha Arunachalam. "Word learning mechanisms." Wiley Interdisciplinary Reviews: Cognitive Science 8, no. 4 (February 3, 2017): e1435. http://dx.doi.org/10.1002/wcs.1435.

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Goldstein, Irwin. "Learning the Word `Toothache'." Philosophy and Phenomenological Research 46, no. 2 (December 1985): 337. http://dx.doi.org/10.2307/2107363.

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Coran, Monica, Antoni Rodriguez-Fornells, Neus Ramos-Escobar, Matti Laine, and Nadine Martin. "Word Learning in Aphasia." Topics in Language Disorders 40, no. 1 (2020): 81–109. http://dx.doi.org/10.1097/tld.0000000000000204.

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Banerjee, Abhijit, and Drew Fudenberg. "Word-of-mouth learning." Games and Economic Behavior 46, no. 1 (January 2004): 1–22. http://dx.doi.org/10.1016/s0899-8256(03)00048-4.

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BLOOM, P. "Intentionality and word learning." Trends in Cognitive Sciences 1, no. 1 (April 1997): 9–12. http://dx.doi.org/10.1016/s1364-6613(97)01006-1.

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Bloom, Paul, and Lori Markson. "Capacities underlying word learning." Trends in Cognitive Sciences 2, no. 2 (February 1998): 67–73. http://dx.doi.org/10.1016/s1364-6613(98)01121-8.

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Markman, Ellen M., and Maxim Abelev. "Word learning in dogs?" Trends in Cognitive Sciences 8, no. 11 (November 2004): 479–81. http://dx.doi.org/10.1016/j.tics.2004.09.007.

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Nelson, Katherine. "Constraints on word learning?" Cognitive Development 3, no. 3 (July 1988): 221–46. http://dx.doi.org/10.1016/0885-2014(88)90010-x.

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Dissertations / Theses on the topic "Word-Learning"

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Zhang, Zheng. "Explorations in Word Embeddings : graph-based word embedding learning and cross-lingual contextual word embedding learning." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS369/document.

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Les plongements lexicaux sont un composant standard des architectures modernes de traitement automatique des langues (TAL). Chaque fois qu'une avancée est obtenue dans l'apprentissage de plongements lexicaux, la grande majorité des tâches de traitement automatique des langues, telles que l'étiquetage morphosyntaxique, la reconnaissance d'entités nommées, la recherche de réponses à des questions, ou l'inférence textuelle, peuvent en bénéficier. Ce travail explore la question de l'amélioration de la qualité de plongements lexicaux monolingues appris par des modèles prédictifs et celle de la mise en correspondance entre langues de plongements lexicaux contextuels créés par des modèles préentraînés de représentation de la langue comme ELMo ou BERT.Pour l'apprentissage de plongements lexicaux monolingues, je prends en compte des informations globales au corpus et génère une distribution de bruit différente pour l'échantillonnage d'exemples négatifs dans word2vec. Dans ce but, je précalcule des statistiques de cooccurrence entre mots avec corpus2graph, un paquet Python en source ouverte orienté vers les applications en TAL : il génère efficacement un graphe de cooccurrence à partir d'un grand corpus, et lui applique des algorithmes de graphes tels que les marches aléatoires. Pour la mise en correspondance translingue de plongements lexicaux, je relie les plongements lexicaux contextuels à des plongements de sens de mots. L'algorithme amélioré de création d'ancres que je propose étend également la portée des algorithmes de mise en correspondance de plongements lexicaux du cas non-contextuel au cas des plongements contextuels
Word embeddings are a standard component of modern natural language processing architectures. Every time there is a breakthrough in word embedding learning, the vast majority of natural language processing tasks, such as POS-tagging, named entity recognition (NER), question answering, natural language inference, can benefit from it. This work addresses the question of how to improve the quality of monolingual word embeddings learned by prediction-based models and how to map contextual word embeddings generated by pretrained language representation models like ELMo or BERT across different languages.For monolingual word embedding learning, I take into account global, corpus-level information and generate a different noise distribution for negative sampling in word2vec. In this purpose I pre-compute word co-occurrence statistics with corpus2graph, an open-source NLP-application-oriented Python package that I developed: it efficiently generates a word co-occurrence network from a large corpus, and applies to it network algorithms such as random walks. For cross-lingual contextual word embedding mapping, I link contextual word embeddings to word sense embeddings. The improved anchor generation algorithm that I propose also expands the scope of word embedding mapping algorithms from context independent to contextual word embeddings
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Schafer, Graham. "Word learning in infancy." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242032.

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Yao, Xin. "Word Learning in Context." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1291060246.

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Rossi, Sonja. "Neuroplasticity of word learning." Doctoral thesis, Humboldt-Universität zu Berlin, 2018. http://dx.doi.org/10.18452/19420.

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Das Wortlernen begleitet unser Leben von der Kindheit bis ins Alter. Kleinkinder lernen ihre Muttersprache(n), aber auch Erwachsene lernen neue Wörter, z.B. beim Fremdspracherwerb. Unter gewissen Umständen muss eine neue Sprache wieder erlernen werden, wie z.B. nach einer Gehirnläsion. Wie meistert unser Gehirn diese herausfordernden Wortlernsituationen? Um die Neuroplastizität des Wortlernens zu untersuchen, wurden unterschiedliche neurowissenschaftliche Methoden (Elektroenzephalographie, funktionelle Nahinfrarotspektroskopie, voxel-basierte Läsion-Verhalten/EEG Mapping), teilweise in Kombination, bei Kleinkindern, Kindern und Erwachsenen sowie Patienten mit einer Gehirnläsion im Vergleich zu älteren Kontrollprobanden angewendet. 5 Experimente untersuchten die neuronale Verarbeitung von Pseudowörtern, welche mutter- und fremdsprachlichen phonotaktischen Regeln (d.h. die Kombination von verschiedenen Phonemen) folgten, in unterschiedlichen Lernsettings bei monolingualen Teilnehmern. Gesunde Erwachsene aber auch 6monatige und ältere Teilnehmer und Patienten konnten diese Regeln differenzieren. Beteiligte Gehirnareale umfassten ein links-hemisphärisches fronto-temporales Netzwerk. Die Verarbeitung universeller Spracheigenschaften, andererseits, zeigte sich in parietalen Regionen. Während Erwachsene eine klare Dominanz der linken Hemisphäre aufwiesen, nutzten 6monatige noch beide Gehirnhälften. Unterschiedliche Sprachtrainings (semantische Trainings oder Passives Zuhören) an drei aufeinanderfolgenden Tagen veränderten auch die Gehirnaktivität der Kleinkinder und der Erwachsenen und wiesen auf eine erhöhte Lernflexibilität hin. Im 6. Experiment lernten 5jährige bilinguale Kinder anhand pragmatischer Eigenschaften neue Adjektive und zeigten effizientere neuronale Mechanismen als Monolinguale. Die Ergebnisse unterstreichen die Wichtigkeit multi-methodologischer Ansätze, um genauere Einblicke in die komplexen Mechanismen der Neuroplastizität zu erlangen.
Word learning accompanies our everyday life from infancy to advanced age. Infants have to learn the native language(s) but also during adulthood word learning can take place, for example if we learn a new foreign language. Sometimes people are confronted with a situation in which they have to re-learn a language because of a brain lesion. How does the brain master these challenging word learning settings? To assess neuroplasticity of word learning several neuroscientific methods (electroencephalography, functional near-infrared spectroscopy, voxel-based lesion-behavior/EEG mapping), partially in combination, were used in infants, children, and adults as well as in patients suffering from a brain lesion compared to matched elderly controls. In 5 experiments neuronal processing of pseudowords corresponding to native and non-native phonotactic rules (i.e., the combination of different phonemes) was investigated under different learning conditions in monolingual participants. Healthy adults but also 6-month-old infants and elderly subjects and patients were able to differentiate these rules. Involved brain areas included a left-hemispheric network of fronto-temporal regions. When processing universal linguistic features, however, more parietal regions were involved. While adults revealed a clear left-dominant network, 6-month-olds still recruited bilateral brain areas. Differential language trainings (semantic or passive listening trainings) over three consecutive days also modulated brain activation in both infants and adults suggesting a high flexibility for learning native and non-native linguistic regularities. In a 6th experiment, bilingual 5-year-old children learned novel adjectives by means of pragmatic cues and revealed more efficient neuronal mechanisms compared to monolingual children. Findings underline the importance of multi-methodological approaches to get clearer insights into the complex machinery of neuroplasticity.
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Packard, Stephanie Leona. "Phonological word-form learning." Thesis, University of Iowa, 2010. https://ir.uiowa.edu/etd/568.

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Seven experiments examined phonological word-form learning (i.e., the learning of novel wordlike sound patterns) after differing types of training. In each case, learning at the end of training was assessed via stem-completion ability. Experiment 1 presented participants with 11 epochs of listening and repeating (incidental learning) and found significant stem-completion ability. The results of Experiment 2 showed greater stem-completion ability after 11 epochs of listening, repeating, and stem-completion testing (deliberate learning). Experiment 3 replicated results from Experiments 1 and 2 in a within-subject design and demonstrated that learning of both types is item-specific and not merely the result of generalized task facilitation. Experiment 4 measured stem-completion ability after 100 epochs of incidental learning and found that it remained lower than after only 11 blocks of deliberate learning in Experiments 2 and 3. Experiments 4, 5, and 6 utilized monosyllabic nonword stimuli, in contrast to the disyllabic nonword stimuli utilized in the first four experiments, and replicated results from Experiments 1, 2, and 3, respectively. Taken together, these results suggest that incidental learning does not yield full mastery of phonological word-forms.
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Tan, Seok Hui. "Factors in infant word learning." Thesis, University of Reading, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252252.

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Dolena, Alexis Lynn. "Uncovering the "slow mapping" process of word learning through word definition and word association tasks." Click here for download, 2006. http://proquest.umi.com/pqdweb?did=1212794661&sid=1&Fmt=2&clientId=3260&RQT=309&VName=PQD.

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Ballem, Kate Drury. "Phonological specificity in early word learning." Thesis, University of Oxford, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410618.

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Frank, Michael C. Ph D. Massachusetts Institute of Technology. "Early word learning through communicative inference." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62045.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 109-122).
How do children learn their first words? Do they do it by gradually accumulating information about the co-occurrence of words and their referents over time, or are words learned via quick social inferences linking what speakers are looking at, pointing to, and talking about? Both of these conceptions of early word learning are supported by empirical data. This thesis presents a computational and theoretical framework for unifying these two different ideas by suggesting that early word learning can best be described as a process of joint inferences about speakers' referential intentions and the meanings of words. Chapter 1 describes previous empirical and computational research on "statistical learning"--the ability of learners to use distributional patterns in their language input to learn about the elements and structure of language-and argues that capturing this abifity requires models of learning that describe inferences over structured representations, not just simple statistics. Chapter 2 argues that social signals of speakers' intentions, even eye-gaze and pointing, are at best noisy markers of reference and that in order to take advantage of these signals fully, learners must integrate information across time. Chapter 3 describes the kinds of inferences that learners can make by assuming that speakers are informative with respect to their intended meaning, introducing and testing a formalization of how Grice's pragmatic maxims can be used for word learning. Chapter 4 presents a model of cross-situational intentional word learning that both learns words and infers speakers' referential intentions from labeled corpus data.
by Michael C. Frank.
Ph.D.
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Stickgold, Eli (Eli B. ). "Word sense disambiguation through lattice learning." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66811.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 51).
The question of how a computer reading a text can go from a word to its meaning is an open and difficult one. The WordNet[3] lexical database uses a system of nested supersets to allow programs to be specific as to what meaning of a word they are using, but a system that picks the correct meaning is still necessary. In an attempt to capture the human understanding of this problem and produce a system that can achieve this goal with minimal starting information, I created the DISAMBIGUATOR program. DISAMBIGUATOR uses Lattice Learning to capture the concept of contexts, which represent common situations that multiple words are found in, and uses Genesis' system of Things, Sequences, Derivative and Relations to understand some contexts as being related to others (i.e. that 'things which can fly to a tree' and 'things which can fly to Spain' are related in that they are both special cases of the context 'things which can fly'). Using this system, DISAMBIGUATOR can tell us which meaning of 'hawk' we should use if we see it in a sentence like 'the hawk flew to the tree.' DISAMBIGUATOR is implemented in Java as part of the Genesis system, and can disambiguate short stories of around ten related statements with only a single query to the user.
by Eli Stickgold.
M.Eng.
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Books on the topic "Word-Learning"

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Weixel, Suzanne. Learning Word 2000. New York: DDC Pub., 1999.

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Roman, Steven. Learning Word programming. Sebastopol, CA: O'Reilly, 1998.

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Learning Microsoft Word 2000. New York, NY: DDC Pub., 2000.

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Kathy, Berkemeyer, ed. Learning Microsoft Word 97. New York: DDC Pub., 1998.

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Shelly, Gary B. Learning to use Word Perfect. [Boston, MA]: Boyd & Fraser, 1989.

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Estes, Daniel J. Learning and living Godʼs word. Schaumburg, Ill: Regular Baptist Press, 1993.

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University of North London. IT Learning Exchange., ed. Microsoft Word 97: Learning module. London: University of North London, 1998.

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Harris, Colin. Teacher's resource book: Word learning. Oxford: Oxford University Press, 1996.

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University of North London. IT Learning Exchange., ed. Microsoft Word 97: Learning module. London: University of North London, 1997.

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Lisa, Bucki, ed. Learning word processing: Projects & exercises. New York, NY: DDC Pub., 2001.

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Book chapters on the topic "Word-Learning"

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Scofield, Jason. "Word Learning." In Encyclopedia of the Sciences of Learning, 3463–65. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_805.

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Borghi, Anna M., and Ferdinand Binkofski. "Word Learning and Word Acquisition." In SpringerBriefs in Psychology, 71–93. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-9539-0_4.

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Andrews, Anne M., Greg A. Gerhardt, Lynette C. Daws, Mohammed Shoaib, Barbara J. Mason, Charles J. Heyser, Luis De Lecea, et al. "Novel Word Learning." In Encyclopedia of Psychopharmacology, 906. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_3441.

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, Tamás Horváth, Stefan Wrobel, Kevin B. Korb, William Stafford Noble, et al. "Learning Word Senses." In Encyclopedia of Machine Learning, 595. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_467.

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Singh, Leher. "Early Word Recognition and Word Learning in Mandarin Learning Children." In Speech Perception, Production and Acquisition, 199–218. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7606-5_11.

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Lei, Chen. "Unsupervised Learning: Word Vector." In Cognitive Intelligence and Robotics, 95–149. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2233-5_7.

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Li, Qi, Tianshi Li, and Baobao Chang. "Learning Word Sense Embeddings from Word Sense Definitions." In Natural Language Understanding and Intelligent Applications, 224–35. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-50496-4_19.

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Bosch, Laura, Maria Teixidó, and Thais Agut Quijano. "Word segmentation and mapping in early word learning." In Atypical Language Development in Romance Languages, 75–90. Amsterdam: John Benjamins Publishing Company, 2019. http://dx.doi.org/10.1075/z.223.05bos.

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Grassmann, Susanne. "The pragmatics of word learning." In Pragmatic Development in First Language Acquisition, 139–60. Amsterdam: John Benjamins Publishing Company, 2014. http://dx.doi.org/10.1075/tilar.10.09gra.

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Joffe, Victoria L., and Hilary Lowe. "Word learning and its challenges." In Enriching Vocabulary in Secondary Schools, 15–20. London: Routledge, 2022. http://dx.doi.org/10.4324/9780429433177-3.

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Conference papers on the topic "Word-Learning"

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Yin, Wenpeng, and Hinrich Schütze. "Learning Word Meta-Embeddings." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-1128.

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Muslim, Aji, Anik Ghufron, and Wuri Wuryandani. "Student Learning Motivation: Word Square Learning Model." In Proceedings of the 1st International Conference on Social Sciences, ICONESS 2021, 19 July 2021, Purwokerto, Central Java, Indonesia. EAI, 2021. http://dx.doi.org/10.4108/eai.19-7-2021.2313467.

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Fadaee, Marzieh, Arianna Bisazza, and Christof Monz. "Learning Topic-Sensitive Word Representations." In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-2070.

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Samardzhiev, Krasen, Andrew Gargett, and Danushka Bollegala. "Learning Neural Word Salience Scores." In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/s18-2004.

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Sen, Mehmet Umut, and Hakan Erdogan. "Learning word representations for Turkish." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830586.

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Zhao, Jieyu, Yichao Zhou, Zeyu Li, Wei Wang, and Kai-Wei Chang. "Learning Gender-Neutral Word Embeddings." In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/d18-1521.

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Song, Yan, and Shuming Shi. "Complementary Learning of Word Embeddings." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/607.

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Continuous bag-of-words (CB) and skip-gram (SG) models are popular approaches to training word embeddings. Conventionally they are two standing-alone techniques used individually. However, with the same goal of building embeddings by leveraging surrounding words, they are in fact a pair of complementary tasks where the output of one model can be used as input of the other, and vice versa. In this paper, we propose complementary learning of word embeddings based on the CB and SG model. Specifically, one round of learning first integrates the predicted output of a SG model with existing context, then forms an enlarged context as input to the CB model. Final models are obtained through several rounds of parameter updating. Experimental results indicate that our approach can effectively improve the quality of initial embeddings, in terms of intrinsic and extrinsic evaluations.
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Alishahi, Afra, Afsaneh Fazly, and Suzanne Stevenson. "Fast mapping in word learning." In the Twelfth Conference. Morristown, NJ, USA: Association for Computational Linguistics, 2008. http://dx.doi.org/10.3115/1596324.1596335.

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HANNAGAN, T., and J. GRAINGER. "LEARNING THE VISUAL WORD CODE." In Proceedings of the 12th Neural Computation and Psychology Workshop. WORLD SCIENTIFIC, 2011. http://dx.doi.org/10.1142/9789814340359_0010.

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Jawanpuria, Pratik, Satya Dev N T V, Anoop Kunchukuttan, and Bamdev Mishra. "Learning Geometric Word Meta-Embeddings." In Proceedings of the 5th Workshop on Representation Learning for NLP. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.repl4nlp-1.6.

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Reports on the topic "Word-Learning"

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Piper, Benjamin, Yasmin Sitabkhan, Jessica Mejia, and Kellie Betts. Effectiveness of Teachers’ Guides in the Global South: Scripting, Learning Outcomes, and Classroom Utilization. RTI Press, May 2018. http://dx.doi.org/10.3768/rtipress.2018.op.0053.1805.

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This report presents the results of RTI International Education’s study on teachers' guides across 13 countries and 19 projects. Using quantitative and qualitative methods, we examine how teachers’ guides across the projects differ and find substantial variation in the design and structure of the documents. We develop a scripting index so that the scripting levels of the guides can be compared across projects. The impact results of the programs that use teachers’ guides show significant impacts on learning outcomes, associated with approximately an additional half year of learning, showing that structured teachers’ guides contribute to improved learning outcomes. During observations, we find that teachers make a variety of changes in their classroom instruction from how the guides are written, showing that the utilization of structured teachers’ guides do not create robotic teachers unable to use their own professional skills to teach children. Unfortunately, many changes that teachers make reduce the amount of group work and interactivity that was described in the guides, suggesting that programs should encourage teachers to more heavily utilize the instructional routines designed in the guide. The report includes a set of research-based guidelines that material developers can use to develop teachers’ guides that will support effective instructional practices and help improve learning outcomes. The key takeaway from the report is that structured teachers' guides improve learning outcomes, but that overly scripted teachers' guides are somewhat less effective than simplified teachers' guides that give specific guidance to the teacher but are not written word for word for each lesson in the guide.
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PODDUBSKAYA, O., V. DARJINA, and E. MAKSIMKINA. PECULIARITIES OF STORITELLING APPLICATION FOR SPEECH DEVELOPMENT OF FUTURE FOREIGN LANGUAGE TEACHERS. Science and Innovation Center Publishing House, 2022. http://dx.doi.org/10.12731/2658-4034-2022-13-2-3-7-15.

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The article is devoted to the study of storytelling possibilities as a means of speech development, including the ones in a foreign language. This determines its relevance, since fluency in the word is necessary for a teacher to solve learning problems and create an atmosphere of interest, creativity and psychological contact with students.
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Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4431.

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The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glosbe, Collins Dictionary, Longman Dictionary, Oxford Dictionary. As a result of the deep analysis of these online dictionaries, we found out they have the next standard functions like the word explanations, transcription, audio pronounce, semantic connections, and examples of use. In propose dictionaries, we also found out the additional tools of learning foreign languages (mostly English) that can be effective. In general, we described sixteen functions of the online platforms for learning that can be useful in learning a foreign language. We have compiled a comparison table based on the next functions: machine translation, multilingualism, a video of pronunciation, an image of a word, discussion, collaborative edit, the rank of words, hints, learning tools, thesaurus, paid services, sharing content, hyperlinks in a definition, registration, lists of words, mobile version, etc. Based on the additional tools of online dictionaries we created a diagram that shows the functionality of analyzed platforms.
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Bhattacharjea, Suman, Sehar Saeed, Rajib Timalsina, and Syeed Ahamed. Citizen-led Assessments: A Model for Evidence-based Advocacy and Action to Improve Learning. Australian Council for Educational Research, June 2021. http://dx.doi.org/10.37517/978-1-74286-636-9.

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Designed as household-based assessments, citizen-led assessments (CLAs) are implemented by local organizations who assess children in their homes, thus reaching the most marginalized children, families, and communities, often in remote areas. CLAs add an essential piece of information for truly monitoring progress and help realistically represent the learning levels of all children – at national, regional, and global levels. By using simple tools and easy-to-understand reports, CLAs engage parents and community members in discussions about learning and help foster understanding of the importance of ensuring quality education through civil action. In this publication, members from organizations conducting CLAs in India, Pakistan, Nepal and Bangladesh provide an overview of the CLA model and illustrate a range of ways in which the model has been implemented in the four South Asian countries to monitor and improve learning. In all four countries, the initiative is known as the Annual Status of Education Report, or ASER – a word that means ‘impact’ in three of these four countries. By design, ASER assesses foundational reading and numeracy skills.
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NARYKOVA, N. A., S. V. KHATAGOVA, and Yu R. PEREPELITSYNA. PEJORATIVE WORDS IN GERMAN MASS-MEDIA IN NOMINATIONS OF POLITICIANS. Science and Innovation Center Publishing House, April 2022. http://dx.doi.org/10.12731/2077-1770-2021-14-1-3-57-68.

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One of the main functions of mass media is influence on public opinion. So emotionally-painted lexical means are widely used in mass media in relation to leading politicians who are the centre of political arena. They are exposed to the frequent criticism, a negative estimation. The present article is devoted to the consideration of pejorative lexicon which is applied in nominations for heads of states. An empirical material of research were electronic newspapers and editions: Der Spiegel, Die Zeit, Sueddeutsche Zeitung, Der Tagesspiegel, taz, Die Welt, Gegenblende. As the basic methods of research are the following: the componental analysis, the lexico-semantic analysis, the stylistic analysis. The result of research revealed, that in German mass media there is a significant amount of persons names pejorative colouring. They express censure, disrespect, sneer, hatred, antipathy, condemnation, mistrust and so on. There main word-formations for persons nominations are composition, a derivation with using of suffixes and subsuffixes, attributive word-combinations, metaphorically-metonymical way. The materials of the research work can be used in the course of learning German language, at the practical training in oral speech, and also in the course of lexicology, general and aspect lexicography.
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Nelson, Gena, Angela Crawford, and Jessica Hunt. A Systematic Review of Research Syntheses for Students with Mathematics Learning Disabilities and Difficulties. Boise State University, Albertsons Library, January 2022. http://dx.doi.org/10.18122/sped.143.boisestate.

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The purpose of this document is to provide readers with the coding protocol that authors used to code 36 research syntheses (including meta-analyses, evidence-based reviews, and quantitative systematic reviews) focused on mathematics interventions for students with learning disabilities (LD), mathematics learning disabilities (MLD), and mathematics difficulties (MD). The purpose of the systematic review of mathematics intervention syntheses was to identify patterns and gaps in content areas, instructional strategies, effect sizes, and definitions of LD, MLD, and MD. We searched the literature for research syntheses published between 2000 and 2020 and used rigorous inclusion criteria in our literature review process. We evaluated 36 syntheses that included 836 studies with 32,495 participants. We coded each synthesis for variables across seven categories including: publication codes (authors, year, journal), inclusion and exclusion criteria, content area focus, instructional strategy focus, sample size, methodological information, and results. The mean interrater reliability across all codes using this coding protocol was 90.3%. Although each synthesis stated a focus on LD, MLD, or MD, very few students with LD or MLD were included, and authors’ operational definitions of disability and risk varied. Syntheses predominantly focused on word problem solving, fractions, computer- assisted learning, and schema-based instruction. Syntheses reported wide variation in effectiveness, content areas, and instructional strategies. Finally, our results indicate the majority of syntheses report achievement outcomes, but very few syntheses report on other outcomes (e.g., social validity, strategy use). We discuss how the results of this comprehensive review can guide researchers in expanding the knowledge base on mathematics interventions. The systematic review that results from this coding process is accepted for publication and in press at Learning Disabilities Research and Practice.
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Moreno Pérez, Carlos, and Marco Minozzo. “Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy. Madrid: Banco de España, November 2022. http://dx.doi.org/10.53479/23646.

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This paper investigates the relationship between the views expressed in the minutes of the meetings of the Central Bank of Brazil’s Monetary Policy Committee (COPOM) and the real economy. It applies various computational linguistic machine learning algorithms to construct measures of the minutes of the COPOM. First, we create measures of the content of the paragraphs of the minutes using Latent Dirichlet Allocation (LDA). Second, we build an uncertainty index for the minutes using Word Embedding and K-Means. Then, we combine these indices to create two topic-uncertainty indices. The first one is constructed from paragraphs with a higher probability of topics related to “general economic conditions”. The second topic-uncertainty index is constructed from paragraphs that have a higher probability of topics related to “inflation” and the “monetary policy discussion”. Finally, we employ a structural VAR model to explore the lasting effects of these uncertainty indices on certain Brazilian macroeconomic variables. Our results show that greater uncertainty leads to a decline in inflation, the exchange rate, industrial production and retail trade in the period from January 2000 to July 2019.
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Children with ASD show intact statistical word learning. ACAMH, October 2018. http://dx.doi.org/10.13056/acamh.10588.

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