Journal articles: 'Language processing tasks'

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Sun, Tian-Xiang, Xiang-Yang Liu, Xi-Peng Qiu, and Xuan-Jing Huang. "Paradigm Shift in Natural Language Processing." Machine Intelligence Research 19, no. 3 (May 28, 2022): 169–83. http://dx.doi.org/10.1007/s11633-022-1331-6.

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AbstractIn the era of deep learning, modeling for most natural language processing (NLP) tasks has converged into several mainstream paradigms. For example, we usually adopt the sequence labeling paradigm to solve a bundle of tasks such as POS-tagging, named entity recognition (NER), and chunking, and adopt the classification paradigm to solve tasks like sentiment analysis. With the rapid progress of pre-trained language models, recent years have witnessed a rising trend of paradigm shift, which is solving one NLP task in a new paradigm by reformulating the task. The paradigm shift has achieved great success on many tasks and is becoming a promising way to improve model performance. Moreover, some of these paradigms have shown great potential to unify a large number of NLP tasks, making it possible to build a single model to handle diverse tasks. In this paper, we review such phenomenon of paradigm shifts in recent years, highlighting several paradigms that have the potential to solve different NLP tasks.

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Kachkou, Dz I. "Applying the language acquisition model to the solution small language processing tasks." Informatics 19, no. 1 (January 5, 2022): 96–110. http://dx.doi.org/10.37661/1816-0301-2022-19-1-96-110.

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The problem of building a computer model of a small language was under solution. The relevance of this task is due to the following considerations: the need to eliminate the information inequality between speakers of different languages; the need for new tools for the study of poorly understood languages, as well as innovative approaches to language modeling in the low-resource context; the problem of supporting and developing small languages.There are three main objectives in solving the problem of small natural language processing at the stage of describing the problem situation: to justify the problem of modeling language in the context of resource scarcity as a special task in the field of natural languages processing, to review the literature on the relevant topic, to develop the concept of language acquisition model with a relatively small number of available resources. Computer modeling techniques using neural networks, semi-supervised learning and reinforcement learning were involved.The paper provides a review of the literature on modeling the learning of vocabulary, morphology, and grammar of a child's native language. Based on the current understanding of the language acquisition and existing computer models of this process, the architecture of the system of small language processing, which is taught through modeling of ontogenesis, is proposed. The main components of the system and the principles of their interaction are highlighted. The system is based on a module built on the basis of modern dialogical language models and taught in some rich-resources language (e.g., English). During training, an intermediate layer is used which represents statements in some abstract form, for example, in the symbols of formal semantics. The relationship between the formal recording of utterances and their translation into the target low-resource language is learned by modeling the child's acquisition of vocabulary and grammar of the language. One of components stands for the non-linguistic context in which language learning takes place.This article explores the problem of modeling small languages. A detailed substantiation of the relevance of modeling small languages is given: the social significance of the problem is noted, the benefits for linguistics, ethnography, ethnology and cultural anthropology are shown. The ineffectiveness of approaches applied to large languages in conditions of a lack of resources is noted. A model of language learning by means of ontogenesis simulation is proposed, which is based both on the results obtained in the field of computer modeling and on the data of psycholinguistics.

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Xiao, Yijun, and William Yang Wang. "Quantifying Uncertainties in Natural Language Processing Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7322–29. http://dx.doi.org/10.1609/aaai.v33i01.33017322.

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Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper, we propose novel methods to study the benefits of characterizing model and data uncertainties for natural language processing (NLP) tasks. With empirical experiments on sentiment analysis, named entity recognition, and language modeling using convolutional and recurrent neural network models, we show that explicitly modeling uncertainties is not only necessary to measure output confidence levels, but also useful at enhancing model performances in various NLP tasks.

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Hwa-Froelich, Deborah A., and Hisako Matsuo. "Vietnamese Children and Language-Based Processing Tasks." Language, Speech, and Hearing Services in Schools 36, no. 3 (July 2005): 230–43. http://dx.doi.org/10.1044/0161-1461(2005/023).

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Belov, Serey, Daria Zrelova, Petr Zrelov, and Vladimir Korenkov. "Overview of methods for automatic natural language text processing." System Analysis in Science and Education, no. 3 (2020) (September 30, 2020): 8–22. http://dx.doi.org/10.37005/2071-9612-2020-3-8-22.

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This paper provides a brief overview of modern methods and approaches used for automatic processing of text information. In English-language literature, this area of science is called NLP-Natural Language Processing. The very name suggests that the subject of analysis (and for many tasks – and synthesis) are materials presented in one of the natural languages (and for a number of tasks – in several languages simultaneously), i.e. national languages of communication between people. Programming languages are not included in this group. In Russian-language literature, this area is called Computer (or mathematical) linguistics. NLP (computational linguistics) usually includes speech analysis along with text analysis, but in this review speech analysis does not consider. The review used materials from original works, monographs, and a number of articles published the «Open Systems.DBMS» journal.

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Roh, Jihyeon, Sungjin Park, Bo-Kyeong Kim, Sang-Hoon Oh, and Soo-Young Lee. "Unsupervised multi-sense language models for natural language processing tasks." Neural Networks 142 (October 2021): 397–409. http://dx.doi.org/10.1016/j.neunet.2021.05.023.

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Veldhuis, Dorina, and Jeanne Kurvers. "Offline segmentation and online language processing units." Units of Language – Units of Writing 15, no. 2 (August 10, 2012): 165–84. http://dx.doi.org/10.1075/wll.15.2.03vel.

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Although metalinguistic (‘offline’) awareness of words as linguistic units has been related to literacy, it is still uncertain whether literacy also affects the units of language that people process unconsciously (‘online’). In this contribution, we first discuss the characteristics of offline and online tasks, opening up the perspective that such tasks vary in nature along a continuum ranging from more offline to more online. Subsequently, we present a study employing three relatively more offline and two more online tasks which we conducted among 83 preliterate and 121 literate children at Dutch primary schools. The results of the more offline tasks reveal a significant influence of literacy on segmentation along word-boundaries, while the results from the relatively more online tasks are less clear-cut with respect to the way in which literacy affects language processing. Keywords: literacy; language acquisition; metalinguistic awareness; online and offline task; word-segmentation; language processing; developmental psycholinguistics; Dutch

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Commissaire, Eva, Adrian Pasquarella, Becky Xi Chen, and S. Hélène Deacon. "The development of orthographic processing skills in children in early French immersion programs." Written Language and Literacy 17, no. 1 (April 11, 2014): 16–39. http://dx.doi.org/10.1075/wll.17.1.02com.

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Children learning to read in two languages are faced with orthographic features from both languages, either unique to a language or similar across languages. In the present study, we examined how children develop orthographic processing skills over time (from grade 1 to grade 2) with a sample of Canadian children attending a French immersion program and we investigated the underlying factor structure of orthographic skills across English and French. Two orthographic processing tasks were administered in both languages: lexical orthographic processing (e.g. choose the correct spelling from people–peeple) and sub-lexical orthographic processing (e.g. which is the more word-like vaid–vayd?), which included both language-specific and language-shared orthographic regularities. Children’s performances in sub-lexical tasks increased with grade but were comparable across languages. Further, evidence for a one factor model including all measures suggested that there is a common underlying orthographic processing skill that cuts across measurement and language variables. Keywords: orthographic processing; reading; French immersion; bilinguals; second language learners

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Mihaljević Djigunović, Jelena. "Language anxiety and language processing." EUROSLA Yearbook 6 (July 20, 2006): 191–212. http://dx.doi.org/10.1075/eurosla.6.12mih.

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This paper focuses on two studies into the effects of language anxiety on language processing. Using samples of Croatian L1 — English L2 speakers performing two picture description tasks (one in L1 and one in L2), the studies analysed their oral productions in order to identify a number of temporal and hesitation signals of planning processes. The findings suggest that observing learners using audio and video equipment and trying to increase their anxiety through interpersonal style does not produce a significant difference. However, learners watching someone apparently taking notes on their performance seemed to be significantly anxiety provoking. Qualitative analysis suggests that, in comparison with low anxiety language users, high anxiety language users produce longer texts in L2 than in L1, produce smaller amounts of continuous speech in both L1 and L2, produce filled pauses with a higher mean length in L2 than in L1, have longer mid-clause pauses, fewer repetitions, and make more false starts.

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Jagaralmudi, Jagadeesh, Seth Juarez, and Hal Daume. "Kernelized Sorting for Natural Language Processing." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 1020–25. http://dx.doi.org/10.1609/aaai.v24i1.7718.

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Kernelized sorting is an approach for matching objects from two sources (or domains) that does not require any prior notion of similarity between objects across the two sources. Unfortunately, this technique is highly sensitive to initialization and high dimensional data. We present variants of kernelized sorting to increase its robustness and performance on several Natural Language Processing (NLP) tasks: document matching from parallel and comparable corpora, machine transliteration and even image processing. Empirically we show that, on these tasks, a semi-supervised variant of kernelized sorting outperforms matching canonical correlation analysis.

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McKinnis, Sandra, and Molly Thompson. "Altered Auditory Input and Language Webs to Improve Language Processing Skills." Language, Speech, and Hearing Services in Schools 30, no. 3 (July 1999): 302–10. http://dx.doi.org/10.1044/0161-1461.3003.302.

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A method of altering auditory input and a structured set of listening tasks are described. The parameters of rate, prosody, and pattern of pausing are modified in the Altered Auditory Input (AAI) technique to make language input easier for the child with language delays to process. This technique is used during play activities, reading to the child, language activities, faceto-face conversation, and structured listening tasks called Language Webs. The Language Webs are a set of highly redundant, hierarchical picture identification listening tasks. The goal of these approaches is to improve language processing in children with language difficulties so that they can both access their current language knowledge and learn new language.

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EBERT, KERRY DANAHY, GIANG PHAM, and KATHRYN KOHNERT. "Lexical profiles of bilingual children with primary language impairment." Bilingualism: Language and Cognition 17, no. 4 (March 10, 2014): 766–83. http://dx.doi.org/10.1017/s1366728913000825.

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This study used lexical tasks to examine associations between languages, tasks, and age in bilingual children with primary language impairment. Participants (n = 41, mean age 8;8 years) lived in the United States, spoke primarily Spanish (L1) at home and English (L2) at school, and were identified with moderate to severe impairments in both languages. A total of eight tasks (four in each language) measured breadth of vocabulary knowledge (receptive and expressive vocabulary) and aspects of lexical processing (rapid automatic naming and nonword repetition). Correlational analyses revealed older children outperformed younger children on lexical tasks in L2 but not L1, as well as relative L2 dominance for most individuals and tasks. Positive associations were found between languages on processing-based tasks but not vocabulary measures. Findings were consistent with literature on typical bilingual learners, albeit with a notable increased risk of plateau in L1 growth. Results are interpreted within a Dynamic Systems framework.

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Nazzi, Thierry, and Anne Cutler. "How Consonants and Vowels Shape Spoken-Language Recognition." Annual Review of Linguistics 5, no. 1 (January 14, 2019): 25–47. http://dx.doi.org/10.1146/annurev-linguistics-011718-011919.

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All languages instantiate a consonant/vowel contrast. This contrast has processing consequences at different levels of spoken-language recognition throughout the lifespan. In adulthood, lexical processing is more strongly associated with consonant than with vowel processing; this has been demonstrated across 13 languages from seven language families and in a variety of auditory lexical-level tasks (deciding whether a spoken input is a word, spotting a real word embedded in a minimal context, reconstructing a word minimally altered into a pseudoword, learning new words or the “words” of a made-up language), as well as in written-word tasks involving phonological processing. In infancy, a consonant advantage in word learning and recognition is found to emerge during development in some languages, though possibly not in others, revealing that the stronger lexicon–consonant association found in adulthood is learned. Current research is evaluating the relative contribution of the early acquisition of the acoustic/phonetic and lexical properties of the native language in the emergence of this association.

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Ansari, Mohd Zeeshan, Tanvir Ahmad, Mirza Mohd Sufyan Beg, and Noaima Bari. "Language lexicons for Hindi-English multilingual text processing." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (June 1, 2022): 641. http://dx.doi.org/10.11591/ijai.v11.i2.pp641-648.

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<span lang="EN-US">Language identification (LI) in textual documents is the process of automatically detecting the language contained in a document based on its content. The present language identification techniques presume that a document contains text in one of the fixed set of languages. However, this presumption is incorrect when dealing with multilingual document which includes content in more than one possible language. Due to the unavailability of standard corpora for Hindi-English mixed lingual language processing tasks, we propose the language lexicons, a novel kind of lexical database that augments several bilingual language processing tasks. These lexicons are built by learning classifiers over English and transliterated Hindi vocabulary. The designed lexicons possess condensed quantitative characteristics which reflect their linguistic strength in respect of Hindi and English language. On evaluating the lexicons, it is observed that words of the same language tend to cluster together and are separable over language classes. On comparing the classifier performance with existing works, the proposed lexicon models exhibit the better performance.</span>

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Tanenhaus, Michael K., and Sarah Brown-Schmidt. "Language processing in the natural world." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1493 (September 25, 2007): 1105–22. http://dx.doi.org/10.1098/rstb.2007.2162.

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The authors argue that a more complete understanding of how people produce and comprehend language will require investigating real-time spoken-language processing in natural tasks, including those that require goal-oriented unscripted conversation. One promising methodology for such studies is monitoring eye movements as speakers and listeners perform natural tasks. Three lines of research that adopt this approach are reviewed: (i) spoken word recognition in continuous speech, (ii) reference resolution in real-world contexts, and (iii) real-time language processing in interactive conversation. In each domain, results emerge that provide insights which would otherwise be difficult to obtain. These results extend and, in some cases, challenge standard assumptions about language processing.

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Ebert, Kerry Danahy, and Giang Pham. "Including Nonlinguistic Processing Tasks in the Identification of Developmental Language Disorder." American Journal of Speech-Language Pathology 28, no. 3 (August 9, 2019): 932–44. http://dx.doi.org/10.1044/2019_ajslp-idll-18-0208.

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Purpose Accurate identification of developmental language disorder (DLD) remains challenging, particularly for children who speak different dialects, languages, or more than 1 language. Children with DLD, on average, have shown subtle deficits on nonlinguistic cognitive processing tasks, and performance on such tasks may be minimally influenced by language experience. This study explores whether nonlinguistic cognitive processing tasks can contribute to the identification of DLD in children from diverse linguistic backgrounds. Method Study 1 combined data from 4 U.S.-based investigations to yield a sample of 395 children, ages 6–10 years, who spoke only English or both Spanish and English. Study 2 consisted of an international sample of 55 kindergarten children living in Vietnam. Each study included children with DLD and children with typical development. Participants completed nonlinguistic cognitive tasks of processing speed, auditory working memory, and attentional control. Data analysis compared typically developing to DLD groups by age and language background. Then, we empirically derived cut-points to report diagnostic accuracy (sensitivity, specificity, and positive and negative likelihood ratios). Results For all 3 tasks, adequate sensitivity or specificity (but not both in most cases) was achieved in nearly all age groups. Likelihood ratios reached moderately to very informative levels in several instances. Diagnostic results were maintained when monolingual and bilingual samples were combined into a single group. Conclusions Nonlinguistic cognitive processing tasks may contribute to accurate identification of DLD in combination with other measures. Further research is needed to refine tasks, confirm cut-points established here, and extend findings to children from additional language backgrounds.

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Trummer, Immanuel. "Database Tuning using Natural Language Processing." ACM SIGMOD Record 50, no. 3 (December 2021): 27–28. http://dx.doi.org/10.1145/3503780.3503788.

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Introduction. We have seen significant advances in the state of the art in natural language processing (NLP) over the past few years [20]. These advances have been driven by new neural network architectures, in particular the Transformer model [19], as well as the successful application of transfer learning approaches to NLP [13]. Typically, training for specific NLP tasks starts from large language models that have been pre-trained on generic tasks (e.g., predicting obfuscated words in text [5]) for which large amounts of training data are available. Using such models as a starting point reduces task-specific training cost as well as the number of required training samples by orders of magnitude [7]. These advances motivate new use cases for NLP methods in the context of databases.

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Ali, Miss Aliya Anam Shoukat. "AI-Natural Language Processing (NLP)." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 10, 2021): 135–40. http://dx.doi.org/10.22214/ijraset.2021.37293.

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Natural Language Processing (NLP) could be a branch of Artificial Intelligence (AI) that allows machines to know the human language. Its goal is to form systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification. Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It's spread its applications in various fields like computational linguistics, email spam detection, information extraction, summarization, medical, and question answering etc. The goal of the Natural Language Processing is to style and build software system which will analyze, understand, and generate languages that humans use naturally, so as that you just could also be ready to address your computer as if you were addressing another person. Because it’s one amongst the oldest area of research in machine learning it’s employed in major fields like artificial intelligence speech recognition and text processing. Natural language processing has brought major breakthrough within the sector of COMPUTATION AND AI.

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Venkatraman, Vinod, Soon Chun Siong, Michael W. L. Chee, and Daniel Ansari. "Effect of Language Switching on Arithmetic: A Bilingual fMRI Study." Journal of Cognitive Neuroscience 18, no. 1 (January 1, 2006): 64–74. http://dx.doi.org/10.1162/089892906775250030.

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The role of language in performing numerical computations has been a topic of special interest in cognition. The “Triple Code Model” proposes the existence of a language-dependent verbal code involved in retrieving arithmetic facts related to addition and multiplication, and a language-independent analog magnitude code subserving tasks such as number comparison and estimation. Neuroimaging studies have shown dissociation between dependence of arithmetic computations involving exact and approximate processing on language-related circuits. However, a direct manipulation of language using different arithmetic tasks is necessary to assess the role of language in forming arithmetic representations and in solving problems in different languages. In the present study, 20 English-Chinese bilinguals were trained in two unfamiliar arithmetic tasks in one language and scanned using fMRI on the same problems in both languages (English and Chinese). For the exact “base-7 addition” task, language switching effects were found in the left inferior frontal gyrus (LIFG) and left inferior parietal lobule extending to the angular gyrus. In the approximate “percentage estimation” task, language switching effects were found predominantly in the bilateral posterior intraparietal sulcus and LIFG, slightly dorsal to the LIFG activation seen for the base-7 addition task. These results considerably strengthen the notion that exact processing relies on verbal and language-related networks, whereas approximate processing engages parietal circuits typically involved in magnitude-related processing.

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Kosaka, Naoko, Akira Koyama, Tomohiro Kokogawa, Yuji Maeda, Hiroko Koumoto, Shingo Suzuki, Kenshi Yamaguchi, and Kentaro Inui. "Disaster Information System Using Natural Language Processing." Journal of Disaster Research 12, no. 1 (February 1, 2017): 67–78. http://dx.doi.org/10.20965/jdr.2017.p0067.

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In this paper, we analyze incident response management work using free-formed information that is important for incident response communication. We develop a standard operating procedure for the work and study a support method based on a language processing technology to prevent missing or overlapping necessary tasks and optimize management of progress and integration management of related tasks. A prototype system was developed and evaluated in a workshop held by a local government, or in an exercise. As a result, it is confirmed that the use of language processing technology can make incident response management work efficient.

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Huang, Fei, Arun Ahuja, Doug Downey, Yi Yang, Yuhong Guo, and Alexander Yates. "Learning Representations for Weakly Supervised Natural Language Processing Tasks." Computational Linguistics 40, no. 1 (March 2014): 85–120. http://dx.doi.org/10.1162/coli_a_00167.

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Finding the right representations for words is critical for building accurate NLP systems when domain-specific labeled data for the task is scarce. This article investigates novel techniques for extracting features from n-gram models, Hidden Markov Models, and other statistical language models, including a novel Partial Lattice Markov Random Field model. Experiments on part-of-speech tagging and information extraction, among other tasks, indicate that features taken from statistical language models, in combination with more traditional features, outperform traditional representations alone, and that graphical model representations outperform n-gram models, especially on sparse and polysemous words.

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Lagutina, N. S., K. V. Lagutina, A. S. Adrianov, and I. V. Paramonov. "Russian-Language Thesauri: Automatic Construction and Application for Natural Language Processing Tasks." Automatic Control and Computer Sciences 53, no. 7 (December 2019): 705–18. http://dx.doi.org/10.3103/s0146411619070149.

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TAUBE-SCHIFFNORMAN, MARLENE, and NORMAN SEGALOWITZ. "Within-language attention control in second language processing." Bilingualism: Language and Cognition 8, no. 3 (November 15, 2005): 195–206. http://dx.doi.org/10.1017/s1366728905002257.

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This study investigated attention control in tasks involving the processing of relational terms (more highly grammaticized linguistic stimuli: spatial prepositions) and non-relational terms (less highly grammaticized lexical stimuli: nouns) in a first (L1) and second language (L2). Participants were adult bilinguals with greater proficiency in their L1 (English) than in their L2 (French) as determined by self-report and performance on a speeded word classification task. Attention control was operationalized in terms of shift costs obtained in an alternating runs experimental design (Rogers and Monsell, 1995). As hypothesized from consideration of the attention-directing functions of language, participants displayed significantly greater shift costs (lower attention control) for relational terms when performing in the L2 as compared to the L1, but no difference in shift costs for non-relational terms between the two languages. The results are discussed from a cognitive linguistic perspective and in relation to second language proficiency development.

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Pujeri, Bhagyashree P., and Jagadeesh Sai D. "An Anatomization of Language Detection and Translation using NLP Techniques." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (December 10, 2020): 69–77. http://dx.doi.org/10.35940/ijitee.b8265.1210220.

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The issue with identifying language relates to process of determining natural language in which specific text is written. This is one of the big difficulties in the processing of natural languages. Still, they also pose a problem in improving multiclass classification in this area. Language detection and translation a significant Language Identification task are required. The language analysis method may be carried out according to tools available in a particular language if the source language is known. A successful language detection algorithm determines the achievement of the sentiment analysis task and other identification tasks. Processing natural language and machine learning techniques involve knowledge that is annotated with its language. Algorithms for natural language processing must be updated according to language's grammar.This paper proposes a secure language detection and translation technique to solve the security in natural language processing problems. Language detection algorithm based on char n-gram based statistical detector and translation Yandex API is used.While translating, there should be encryption and decryption for that we are using AES Algorithm.

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Nandra, Constantin, and Dorian Gorgan. "DEFINING EARTH DATA BATCH PROCESSING TASKS BY MEANS OF A FLEXIBLE WORKFLOWDESCRIPTION LANGUAGE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 59–66. http://dx.doi.org/10.5194/isprsannals-iii-4-59-2016.

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This paper aims to present some of the main features of the Workflow Description Language (WorDeL) and demonstrate their usage in defining Earth Data processing tasks. This description language is based on the flexible description of processing tasks as workflows, composed of basic processing operators. This approach allows the language to offer an intuitive way of representing processing tasks, without requiring programming expertise from its users. It also allows its users to employ and integrate existing functionality into their design, thereby reducing the design complexity and development effort of newly defined processing workflows. WorDeL supports the transparent adaptive parallelization of the processing tasks over high performance computation architectures, such as cloud-based solutions. Throughout the paper, we will exemplify this language’s use in creating flexible, reusable and easy-to-understand earth data processing descriptions, with an emphasis on satellite image processing.

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Nandra, Constantin, and Dorian Gorgan. "DEFINING EARTH DATA BATCH PROCESSING TASKS BY MEANS OF A FLEXIBLE WORKFLOWDESCRIPTION LANGUAGE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (June 3, 2016): 59–66. http://dx.doi.org/10.5194/isprs-annals-iii-4-59-2016.

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This paper aims to present some of the main features of the Workflow Description Language (WorDeL) and demonstrate their usage in defining Earth Data processing tasks. This description language is based on the flexible description of processing tasks as workflows, composed of basic processing operators. This approach allows the language to offer an intuitive way of representing processing tasks, without requiring programming expertise from its users. It also allows its users to employ and integrate existing functionality into their design, thereby reducing the design complexity and development effort of newly defined processing workflows. WorDeL supports the transparent adaptive parallelization of the processing tasks over high performance computation architectures, such as cloud-based solutions. Throughout the paper, we will exemplify this language’s use in creating flexible, reusable and easy-to-understand earth data processing descriptions, with an emphasis on satellite image processing.

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Gu, Yu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. "Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing." ACM Transactions on Computing for Healthcare 3, no. 1 (January 31, 2022): 1–23. http://dx.doi.org/10.1145/3458754.

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Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. In this article, we challenge this assumption by showing that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. To facilitate this investigation, we compile a comprehensive biomedical NLP benchmark from publicly available datasets. Our experiments show that domain-specific pretraining serves as a solid foundation for a wide range of biomedical NLP tasks, leading to new state-of-the-art results across the board. Further, in conducting a thorough evaluation of modeling choices, both for pretraining and task-specific fine-tuning, we discover that some common practices are unnecessary with BERT models, such as using complex tagging schemes in named entity recognition. To help accelerate research in biomedical NLP, we have released our state-of-the-art pretrained and task-specific models for the community, and created a leaderboard featuring our BLURB benchmark (short for Biomedical Language Understanding & Reasoning Benchmark) at https://aka.ms/BLURB .

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Bialystok, Ellen. "Metalinguistic aspects of bilingual processing." Annual Review of Applied Linguistics 21 (January 2001): 169–81. http://dx.doi.org/10.1017/s0267190501000101.

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This chapter examines differences in metalinguistic development between monolingual and bilingual children in terms of three subcategories: word awareness, syntactic awareness, and phonological awareness. In each case, some studies have reported advantages for bilingual children, but equally, other studies have found either no difference between the groups, or, in some cases, monolingual advantages. In the discussion of each of these areas, the kinds of tasks for which bilingual and monolingual children perform differently are identified. In none of these three subcategories of metalinguistic awareness do bilingual children exhibit a uniform and consistent advantage over monolinguals. An alternate conception of metalinguistic ability is proposed in which two cognitive processes, analysis and control, are directly responsible for task performance. These processes are involved in all metalinguistic tasks but to different degrees. Re-examining the results in this way reveals that bilingual advantages occur reliably on tasks that make high demands on control but are not evident in tasks that make high demands on analysis. The implications of this pattern for metalinguistic ability are considered.

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Lambert, Craig, Judit Kormos, and Danny Minn. "TASK REPETITION AND SECOND LANGUAGE SPEECH PROCESSING." Studies in Second Language Acquisition 39, no. 1 (March 18, 2016): 167–96. http://dx.doi.org/10.1017/s0272263116000085.

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This study examines the relationship between the repetition of oral monologue tasks and immediate gains in L2 fluency. It considers the effect of aural-oral task repetition on speech rate, frequency of clause-final and midclause filled pauses, and overt self-repairs across different task types and proficiency levels and relates these findings to specific stages of L2 speech production (conceptualization, formulation, and monitoring). Thirty-two Japanese learners of English sampled at three levels of proficiency completed three oral communication tasks (instruction, narration, and opinion) six times. Results revealed that immediate aural-oral same task repetition was related to gains in oral fluency regardless of proficiency level or task type. Overall gains in speech rate were the largest across the first three performances of each task type but continued until the fifth performance. More specifically, however, clause-final pauses decreased until the second performance, midclause pauses decreased up to the fourth, and self-repairs decreased only after the fourth performance, indicating that task repetition may have been differentially related to specific stages in the speech production process.

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Sarica, Serhad, and Jianxi Luo. "Stopwords in technical language processing." PLOS ONE 16, no. 8 (August 5, 2021): e0254937. http://dx.doi.org/10.1371/journal.pone.0254937.

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There are increasing applications of natural language processing techniques for information retrieval, indexing, topic modelling and text classification in engineering contexts. A standard component of such tasks is the removal of stopwords, which are uninformative components of the data. While researchers use readily available stopwords lists that are derived from non-technical resources, the technical jargon of engineering fields contains their own highly frequent and uninformative words and there exists no standard stopwords list for technical language processing applications. Here we address this gap by rigorously identifying generic, insignificant, uninformative stopwords in engineering texts beyond the stopwords in general texts, based on the synthesis of alternative statistical measures such as term frequency, inverse document frequency, and entropy, and curating a stopwords dataset ready for technical language processing applications.

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Bovi, Claudio Delli, and Roberto Navigli. "Multilingual semantic dictionaries for natural language processing: The case of BabelNet." Encyclopedia with Semantic Computing and Robotic Intelligence 01, no. 01 (March 2017): 1630015. http://dx.doi.org/10.1142/s2425038416300159.

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Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP). Getting a handle on the various phenomena that regulate the meaning of linguistic utterances can pave the way for solving many compelling and ambitious tasks in the field, from Machine Translation to Question Answering and Information Retrieval. A complete semantic model of language, however, needs first of all reliable building blocks. In the last two decades, research in lexical semantics (which focuses on the meaning of individual linguistic elements, i.e., words and expressions), has produced increasingly comprehensive and effective machine-readable dictionaries in multiple languages: like humans, NLP systems can now leverage these sources of lexical knowledge to discriminate among various senses of a given lexeme, thereby improving their performances on downstream tasks and applications. In this paper, we focus on the case study of BabelNet, a large multilingual encyclopedic dictionary and semantic network, to describe in detail how such knowledge resources are built, improved and exploited for crucial NLP tasks such as Word Sense Disambiguation, Entity Linking and Semantic Similarity.

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Kohnert, Kathryn, Jennifer Windsor, and Dongsun Yim. "Do Language-Based Processing Tasks Separate Children with Language Impairment from Typical Bilinguals?" Learning Disabilities Research and Practice 21, no. 1 (February 2006): 19–29. http://dx.doi.org/10.1111/j.1540-5826.2006.00204.x.

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Guarasci, Raffaele, Giuseppe De Pietro, and Massimo Esposito. "Quantum Natural Language Processing: Challenges and Opportunities." Applied Sciences 12, no. 11 (June 2, 2022): 5651. http://dx.doi.org/10.3390/app12115651.

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The meeting between Natural Language Processing (NLP) and Quantum Computing has been very successful in recent years, leading to the development of several approaches of the so-called Quantum Natural Language Processing (QNLP). This is a hybrid field in which the potential of quantum mechanics is exploited and applied to critical aspects of language processing, involving different NLP tasks. Approaches developed so far span from those that demonstrate the quantum advantage only at the theoretical level to the ones implementing algorithms on quantum hardware. This paper aims to list the approaches developed so far, categorizing them by type, i.e., theoretical work and those implemented on classical or quantum hardware; by task, i.e., general purpose such as syntax-semantic representation or specific NLP tasks, like sentiment analysis or question answering; and by the resource used in the evaluation phase, i.e., whether a benchmark dataset or a custom one has been used. The advantages offered by QNLP are discussed, both in terms of performance and methodology, and some considerations about the possible usage QNLP approaches in the place of state-of-the-art deep learning-based ones are given.

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오소정 and KIMYOUNGTAE. "Multicultural School-aged Children’s Performance in Vocabulary Knowledge-Based Tasks and Language Processing Tasks." Journal of speech-language & hearing disorders 23, no. 1 (March 2014): 55–88. http://dx.doi.org/10.15724/jslhd.2014.23.1.004.

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Krogh, Simone Møller. "Danish-English Bilinguals’ Cognate Processing in L1 and L2 Visual Lexical Decision Tasks." Languages 7, no. 3 (September 1, 2022): 228. http://dx.doi.org/10.3390/languages7030228.

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Previous research and the BIA+ model support the hypothesis of language nonselective access during bilingual word recognition with language-ambiguous words like cognates organized in two distinct lexical representations. This paper adds to the existing literature by investigating how task demands and language proficiency influence cognate processing. Twenty-six Danish-English bilinguals with upper-intermediate to advanced L2 proficiencies performed four visual lexical decision tasks in which stimulus list composition (pure or mixed) and target language (L1 or L2) were varied. This study thus distinguishes itself from other studies by employing a within-subjects design to investigate a bilingual’s two languages. Significant cognate inhibition effects were found in the L2 mixed language condition while none of the other three tasks yielded significant results. Especially the absence of cognate facilitation effects in the L2 pure language condition was remarkable given the findings of previous literature. With reference to the BIA+ model’s assumptions of differing resting level activations for L1 and L2 lexical representations, the impact of L2 proficiency on cognate processing was tested in a post-hoc analysis dividing participants into two groups. This analysis revealed cognate facilitation effects for L2 upper-intermediate bilinguals in the L2 pure language condition while the results of the L1 tasks for both groups of bilinguals remained non-significant. The results therefore suggest that within-subject cognate processing is modulated by L2 proficiency in certain circumstances.

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Agarwal, Oshin, and Ani Nenkova. "Temporal Effects on Pre-trained Models for Language Processing Tasks." Transactions of the Association for Computational Linguistics 10 (2022): 904–21. http://dx.doi.org/10.1162/tacl_a_00497.

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Abstract Keeping the performance of language technologies optimal as time passes is of great practical interest. We study temporal effects on model performance on downstream language tasks, establishing a nuanced terminology for such discussion and identifying factors essential to conduct a robust study. We present experiments for several tasks in English where the label correctness is not dependent on time and demonstrate the importance of distinguishing between temporal model deterioration and temporal domain adaptation for systems using pre-trained representations. We find that, depending on the task, temporal model deterioration is not necessarily a concern. Temporal domain adaptation, however, is beneficial in all cases, with better performance for a given time period possible when the system is trained on temporally more recent data. Therefore, we also examine the efficacy of two approaches for temporal domain adaptation without human annotations on new data. Self-labeling shows consistent improvement and notably, for named entity recognition, leads to better temporal adaptation than even human annotations.

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Ordin, Mikhail, Leona Polyanskaya, and David Soto. "Metacognitive processing in language learning tasks is affected by bilingualism." Journal of Experimental Psychology: Learning, Memory, and Cognition 46, no. 3 (March 2020): 529–38. http://dx.doi.org/10.1037/xlm0000739.

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Linck, Jared A., Noriko Hoshino, and Judith F. Kroll. "Cross-language lexical processes and inhibitory control." Mental Lexicon 3, no. 3 (December 10, 2008): 349–74. http://dx.doi.org/10.1075/ml.3.3.06lin.

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Many recent studies demonstrate that both languages are active when bilinguals and second language (L2) learners are reading, listening, or speaking one language only. The parallel activity of the two languages has been hypothesized to create competition that must be resolved. Models of bilingual lexical access have proposed an inhibitory control mechanism to effectively limit attention to the intended language (e.g., Green, 1998). Critically, other recent research suggests that a lifetime of experience as a bilingual negotiating the competition across the two languages confers a set of benefits to cognitive control processes more generally (e.g., Bialystok, Craik, Klein, & Viswanathan, 2004). However, few studies have examined the consequences of individual differences in inhibitory control for performance on language processing tasks. The goal of the present work was to determine whether there is a relation between enhanced executive function and performance for L2 learners and bilinguals on lexical comprehension and production tasks. Data were analyzed from two studies involving a range of language processing tasks, a working memory measure, and also the Simon task, a nonlinguistic measure of inhibitory control. The results demonstrate that greater working memory resources and enhanced inhibitory control are related to a reduction in cross-language activation in a sentence context word naming task and a picture naming task, respectively. Other factors that may be related to inhibitory control are identified. The implications of these results for models of bilingual lexical comprehension and production are discussed.

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Kabashi, Besim. "A lexicon of Albanian for natural language processing." Lexicographica 34, no. 2018 (January 28, 2019): 239–48. http://dx.doi.org/10.1515/lex-2018-0012.

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AbstractFor many applications in the field of natural language processing, a lexicon is needed. For the Albanian language a lexicon that can be used for these purposes is presented below. The lexicon contains around 75,000 entries, including proper names such as personal, geographical and other names. Each entry includes grammatical information such as parts of speech and other specific information, e.g. inflection classes for nouns, adjectives and verbs. The lexicon is part of a morphological tool, but can also be used as an independent resource for other tasks and applications or can be adapted for them. Sources for the creation and the extension of the presented lexicon include both information from traditional dictionaries, e.g. spelling dictionaries, and a balanced linguistic corpus using corpus-driven methods and tools. The lexicon is still work in progress, but aims to cover basic information for most frequent tasks of natural language processing.

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Kabashi, Besim. "A lexicon of Albanian for natural language processing." Lexicographica 34, no. 1 (December 1, 2018): 239–48. http://dx.doi.org/10.1515/lex-2018-340112.

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AbstractFor many applications in the field of natural language processing, a lexicon is needed. For the Albanian language a lexicon that can be used for these purposes is presented below. The lexicon contains around 75,000 entries, including proper names such as personal, geographical and other names. Each entry includes grammatical information such as parts of speech and other specific information, e.g. inflection classes for nouns, adjectives and verbs. The lexicon is part of a morphological tool, but can also be used as an independent resource for other tasks and applications or can be adapted for them. Sources for the creation and the extension of the presented lexicon include both information from traditional dictionaries, e.g. spelling dictionaries, and a balanced linguistic corpus using corpus- driven methods and tools. The lexicon is still work in progress, but aims to cover basic information for most frequent tasks of natural language processing.

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Jalvingh, Fedor, Roelien Bastiaanse, and Roel Jonkers. "Language Processing in Neurodegenerative Diseases." Zeitschrift für Neuropsychologie 30, no. 1 (March 1, 2019): 49–62. http://dx.doi.org/10.1024/1016-264x/a000245.

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Abstract. Disorders of language and/or communicative abilities in neurodegenerative diseases are a common phenomenon. Over the past few decades, there has been a growing interest in language performance connected to these diseases. To date, studies in the field of language impairments in Alzheimer’s disease (AD), Parkinson’s disease (PD), and frontotemporal lobar degeneration (FTLD) have focused mainly on particular aspects of language processing in the isolated disease or on comparing certain language tasks in two neurodegenerative diseases. To enable a better understanding and comparison of the underlying linguistic deficits in all three disorders, this paper focuses on phonological, semantic, and grammatical processing in each of the disorders. A review of the literature on language processing deficits reveals that phonological, semantic, and grammatical processing is impaired in the early stages of AD, PD, and FTLD, and that the underlying deficits are sometimes linguistic in nature. Language disorders, however, may also reflect cognitive deficits, such as short-term verbal memory impairments, attention deficits, and reduced switching capacities, all of which have an impact on language processing.

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Marton, Klara, and Richard G. Schwartz. "Working Memory Capacity and Language Processes in Children With Specific Language Impairment." Journal of Speech, Language, and Hearing Research 46, no. 5 (October 2003): 1138–53. http://dx.doi.org/10.1044/1092-4388(2003/089).

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This study examined the interaction between working memory and language comprehension in children with specific language impairment (SLI), focusing on the function of the central executive component and its interaction with the phonological loop (A. D. Baddeley, 1986) in complex working memory tasks. Thirteen children with SLI and 13 age-matched (age range=7;0 [years;months] to 10;0) children with typical language development participated. The tasks combined traditional nonword repetition tests and sentence comprehension by using sentences that differed in length and syntactic complexity. The children with SLI exhibited larger processing and attentional capacity limitations than their age-matched peers. Increased word length and syntactic complexity resulted in a large performance decrease in nonword repetition in both groups. There were some variations in the error pattern, which may indicate qualitative differences between the 2 groups. The performance of the children with SLI in nonword repetition, across the different tasks, indicated a limitation in simultaneous processing rather than difficulty in encoding and analyzing the phonological structure of the nonwords. Furthermore, syntactic complexity had a greater effect on performance accuracy than did sentence length.

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Xi, Su Mei. "Application of Natural Language Processing for Information Retrieval." Applied Mechanics and Materials 380-384 (August 2013): 2614–18. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2614.

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Through a comprehensive analysis of using natural language processing in information retrieval, we compared the effects with the various natural language techniques for information retrieval precision in this paper. This is for the tasks of more suitable as well as accurate results of natural language processing.

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JIANG, NAN. "Morphological insensitivity in second language processing." Applied Psycholinguistics 25, no. 4 (October 2004): 603–34. http://dx.doi.org/10.1017/s0142716404001298.

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Two competing explanations exist regarding the nature of morphological difficulty in adult second language acquisition: competence deficit versus performance deficiency. This study tested these explanations by examining English as a second language (ESL) learners' morphological performance in reading comprehension tasks. Chinese ESL speakers were asked to read English sentences for comprehension in three self-paced word by word reading experiments. Their reading times were measured to determine if they were sensitive to idiosyncrasies/disagreement in sentences that do and do not involve the number morpheme. The results show that they are not sensitive to number disagreement, but sensitive to other idiosyncrasies tested. This insensitivity to the number morpheme suggests that their morphological knowledge is not an integrated part of their automatic second language competence.

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Lee, Chanhee, Kisu Yang, Taesun Whang, Chanjun Park, Andrew Matteson, and Heuiseok Lim. "Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models." Applied Sciences 11, no. 5 (February 24, 2021): 1974. http://dx.doi.org/10.3390/app11051974.

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Language model pretraining is an effective method for improving the performance of downstream natural language processing tasks. Even though language modeling is unsupervised and thus collecting data for it is relatively less expensive, it is still a challenging process for languages with limited resources. This results in great technological disparity between high- and low-resource languages for numerous downstream natural language processing tasks. In this paper, we aim to make this technology more accessible by enabling data efficient training of pretrained language models. It is achieved by formulating language modeling of low-resource languages as a domain adaptation task using transformer-based language models pretrained on corpora of high-resource languages. Our novel cross-lingual post-training approach selectively reuses parameters of the language model trained on a high-resource language and post-trains them while learning language-specific parameters in the low-resource language. We also propose implicit translation layers that can learn linguistic differences between languages at a sequence level. To evaluate our method, we post-train a RoBERTa model pretrained in English and conduct a case study for the Korean language. Quantitative results from intrinsic and extrinsic evaluations show that our method outperforms several massively multilingual and monolingual pretrained language models in most settings and improves the data efficiency by a factor of up to 32 compared to monolingual training.

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Esposito, Massimo, Giovanni Luca Masala, Aniello Minutolo, and Marco Pota. "Special Issue “Natural Language Engineering: Methods, Tasks and Applications”." Future Internet 14, no. 4 (March 26, 2022): 106. http://dx.doi.org/10.3390/fi14040106.

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Kovač, Mirjana Matea. "Usporedba brzine govora u materinskome hrvatskome (L1) i stranome engleskome (L2) jeziku." Fluminensia 30, no. 1 (2018): 77–96. http://dx.doi.org/10.31820/f.30.1.8.

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This paper aims at investigating the differences regarding speech rate between the Croatian (L1) and the English language (L2). The selected tasks include descriptions of dynamic entities which require careful planning and a chronological sequence of speech acts in real time, as well as the description of static spatial arrangements and their relations. The speech tasks were first performed in the native language, followed by the performance of the same tasks in the foreign language. A significantly faster speech rate was hypothesized in the foreign language condition for each investigated speech task due to the unautomated nature of L2 speech production mechanisms. The obtained results point to the conclusion that the speech rate is significantly slower in L2 in narrative tasks due to the conceptual complexity of the task. However, no significant differences in the speech rate were found between the two languages in the case of highly structured tasks with shorter duration, or in the case of repeated tasks. Due to the fact that the tasks have already been performed in the native language, the processing demands on the level of conceptualization decrease, freeing up the attentional resources, which, in turn, assist the formulation and articulation stage by reducing the processing pressure. Also, the fact that no significant differences were found might be explained by the existence of strong associative links between L1 and L2, as well as by the occurrence of highly frequent words which can be easily retrieved from the mental lexicon, especially if they have previously been activated in the native language. Introduction of new topics, on the other hand, implies higher cognitive demands in the conceptualization stage, resulting in a slower speech rate in both languages.

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Conti-Ramsden, Gina. "Processing and Linguistic Markers in Young Children With Specific Language Impairment (SLI)." Journal of Speech, Language, and Hearing Research 46, no. 5 (October 2003): 1029–37. http://dx.doi.org/10.1044/1092-4388(2003/082).

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Thirty-two 5-year-old children with specific language impairment (SLI) and 32 chronological age (CA) controls completed 4 tasks that were considered potential positive markers for SLI. Children's performance on 2 linguistic tasks (past tense and noun plurals task) and 2 processing tasks (nonword repetition and digit recall) were examined. This approach allowed the examination of more than 1 type of marker simultaneously, facilitating both comparisons between markers and also the evaluation of combinations of markers in relation to identifying SLI. Children with SLI performed significantly worse than CA controls in all 4 marker tasks. Specificity/sensitivity analysis of the 4 marker tasks revealed nonword repetition and the past tense task to have the best overall accuracy at the 25th and 16th percentile. Finally, stepwise discriminant analysis revealed nonword repetition and past tense marking to be the best markers for identifying young children with SLI.

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Harms, Victoria L., Colleen Cochran, and Lorin J. Elias. "Melody and Language: An Examination of the Relationship Between Complementary Processes." Open Psychology Journal 7, no. 1 (January 24, 2014): 1–8. http://dx.doi.org/10.2174/1874350101407010001.

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It is well accepted that the left and right hemispheres of the brain typically play separate and distinct roles in cognitive processing. Extensive research examining the lateralization of music and language processes has provided a clear and consistent demonstration of this division of processing across the cerebral hemispheres. However, in spite of this line of research examining population-level lateralization of these processes, little focus has been placed on examining the relationship between the two processes. Do these two processes share a common developmental origin that influences their pattern of lateralization, or do independent processes govern their lateralization? In this study we examined the relationship pattern in degree of lateralization between linguistic processing and melody recognition using dichotic-listening tasks. The expected right ear advantage was observed for the linguistic processing task. Additionally, the expected left ear advantage was not observed for the melody recognition task, precluding an informative assessment of complementarity between the two tasks. A positive correlation between the laterality scores on the two tasks suggests a shared processing network for linguistic and melodic processing.

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Zheng, Zhe, Xin-Zheng Lu, Ke-Yin Chen, Yu-Cheng Zhou, and Jia-Rui Lin. "Pretrained domain-specific language model for natural language processing tasks in the AEC domain." Computers in Industry 142 (November 2022): 103733. http://dx.doi.org/10.1016/j.compind.2022.103733.

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Journal articles on the topic 'Language processing tasks'

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