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Journal articles on the topic 'Short text understanding'
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D.Umanandhini*1, &. S.Manimegalai2. "FUZZY SCORE BASED SHORT TEXT UNDERSTANDING FROM CORPUS DATA USING SEMANTIC DISCOVERY." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 12 (2017): 268–73. https://doi.org/10.5281/zenodo.1116682.
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Short text understanding and short text are always more ambiguous. These short texts are produced including Search queries, Tags, Keywords, Conversation or Social posts and containing limited context. Generally short texts do not contain sufficient collection of data to support many state-of-the-art approaches for text mining such as topic modelling. It presents a comprehensive overview of short text understanding. Here we used a novel framework are Text Feature Extraction Algorithm and Fuzzy weighted Vote algorithm First, Text classification based on semantic feature extraction. Its goal is that use semantic feature extraction to improve the performance of classifier. And second, Fuzzy weighted Vote algorithm is the combination of Fuzzy logic and weighted vote algorithm, which means it generates the fuzzy score and then based on this score the weight is calculated during shortening the text. In experimental results, the novel Feature Extraction and voter has higher safety performance than the previous classification algorithms. This proposed criterion can provide almost accurate safety and also a good range of accessibility. We have proved that in problems where the weighted voting distinguish some alternatives and finds the best alternative. Reduced Computation time comparing to other previous process and schemes.
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Ivgi, Maor, Uri Shaham, and Jonathan Berant. "Efficient Long-Text Understanding with Short-Text Models." Transactions of the Association for Computational Linguistics 11 (2023): 284–99. http://dx.doi.org/10.1162/tacl_a_00547.
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Abstract Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific articles, and long documents due to their quadratic complexity. While a myriad of efficient transformer variants have been proposed, they are typically based on custom implementations that require expensive pretraining from scratch. In this work, we propose SLED: SLiding-Encoder and Decoder, a simple approach for processing long sequences that re-uses and leverages battle-tested short-text pretrained LMs. Specifically, we partition the input into overlapping chunks, encode each with a short-text LM encoder and use the pretrained decoder to fuse information across chunks (fusion-in-decoder). We illustrate through controlled experiments that SLED offers a viable strategy for long text understanding and evaluate our approach on SCROLLS, a benchmark with seven datasets across a wide range of language understanding tasks. We find that SLED is competitive with specialized models that are up to 50x larger and require a dedicated and expensive pretraining step.
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M. Katekar, Aparna, and Antara Bhattacharya. "A Survey on Short Text Understanding." International Journal of Engineering Trends and Technology 42, no. 6 (2016): 291–92. http://dx.doi.org/10.14445/22315381/ijett-v42p253.
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Li, Jun, Guimin Huang, Jianheng Chen, and Yabing Wang. "Short Text Understanding Combining Text Conceptualization and Transformer Embedding." IEEE Access 7 (2019): 122183–91. http://dx.doi.org/10.1109/access.2019.2938303.
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Shobana, J., S. Amutha, and M. Murali. "Understanding Short Text Through Lexical Semantic Analysis." IOP Conference Series: Materials Science and Engineering 1130, no. 1 (2021): 012038. http://dx.doi.org/10.1088/1757-899x/1130/1/012038.
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Sun, Yaru, Ying Yang, and Dawei Yang. "Informed Graph Convolution Networks for Multilingual Short Text Understanding." Procedia Computer Science 207 (2022): 90–99. http://dx.doi.org/10.1016/j.procs.2022.09.041.
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Banegas, Darío Luis. "Understanding a reader's attraction to a literary short text." Colombian Applied Linguistics Journal 16, no. 1 (2014): 105. http://dx.doi.org/10.14483/udistrital.jour.calj.2014.1.a09.
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Ji, Lei, Yujing Wang, Botian Shi, Dawei Zhang, Zhongyuan Wang, and Jun Yan. "Microsoft Concept Graph: Mining Semantic Concepts for Short Text Understanding." Data Intelligence 1, no. 3 (2019): 238–70. http://dx.doi.org/10.1162/dint_a_00013.
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Knowlege is important for text-related applications. In this paper, we introduce Microsoft Concept Graph, a knowledge graph engine that provides concept tagging APIs to facilitate the understanding of human languages. Microsoft Concept Graph is built upon Probase, a universal probabilistic taxonomy consisting of instances and concepts mined from the Web. We start by introducing the construction of the knowledge graph through iterative semantic extraction and taxonomy construction procedures, which extract 2.7 million concepts from 1.68 billion Web pages. We then use conceptualization models to represent text in the concept space to empower text-related applications, such as topic search, query recommendation, Web table understanding and Ads relevance. Since the release in 2016, Microsoft Concept Graph has received more than 100,000 pageviews, 2 million API calls and 3,000 registered downloads from 50,000 visitors over 64 countries.
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Valikova, Olga A., and Alena S. Demchenko. "Translingual Literary Text: on Problem of Understanding." Polylinguality and Transcultural Practices 17, no. 3 (2020): 352–62. http://dx.doi.org/10.22363/2618-897x-2020-17-3-352-362.
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The given study covers an actual interdisciplinary issue - Russian language, post-Soviet Russian literature in particular, that includes the otherness of multiple ethnic cultures and creates unique images of the world. In the modern conventional sense, culture is replaced by transculture - a space of interaction and mutual repulsion, intertwinement, constellation, overlapping, flowing of cultures into one another. These processes have no and cant have any solidified, final forms that would be determined once and for all. Therefore, the works created in the aesthetics of transculturation are always unique, be it a literary text, a musical message or a silent arthouse short film speaking the language of negative space. We believe that a transcultural episteme should be used in the process of new thinking formation. A person without any developed pragmatic presupposition is deprived of explanatory knowledge and becomes a victim of the information manipulation embedding a model of confrontational perception of the Other into the collective consciousness. By the given work, we would like to demonstrate a method of working with higher-school students that we called Immersion Reading. Using the works by Russian Germans (in particular, E. Seifert and G. Belger), we describe the stages of readers immersion into a literary work step by step (context verticalization, hermeneutic comment creation, stages of typification and differentiation of texts within a chosen paradigm, synthesis) and then bring up the results of our work with students and doctoral candidates of Russia and Kazakhstan for discussion within Literature and Globalization, Intercultural Communication in Art Dimension lecture courses (author of the courses - Bakhtikireeva, U.M.).
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Abdalgader, Khaled, Atheer A. Matroud, and Ghaleb Al-Doboni. "Temporal Dynamics in Short Text Classification: Enhancing Semantic Understanding Through Time-Aware Model." Information 16, no. 3 (2025): 214. https://doi.org/10.3390/info16030214.
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Traditional text classification models predominantly rely on static text representations, failing to capture temporal variations in language usage and evolving semantic meanings. This limitation reduces their ability to accurately classify time-sensitive texts, where understanding context, detecting trends, and addressing semantic shifts over time are critical. This paper introduces a novel time-aware short text classification model incorporating temporal information, enabling tracking of and adaptation to evolving language semantics. The proposed model enhances contextual understanding by leveraging timestamps and significantly improves classification accuracy, particularly for time-sensitive applications such as News topic classification. The model employs a hybrid architecture combining Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks, enriched with attention mechanisms to capture both local and global dependencies. To further refine semantic representation and mitigate the effects of semantic drift, the model fine-tunes GloVe embeddings and employs synonym-based data augmentation. The proposed approach is evaluated on three benchmark dynamic datasets, achieving superior performance with classification accuracy reaching 92% for the first two datasets and 85% for the third dataset. Furthermore, the model is applied to a different-fields categorization and trend analysis task, demonstrating its capability to capture temporal patterns and perform detailed trend analysis of domain-agnostic textual content. These results underscore the potential of the proposed framework to provide deeper insights into the evolving nature of language and its impact on short-text classification. This work advances natural language processing by offering a comprehensive time-aware classification framework, addressing the challenges of temporal dynamics in language semantics.
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Assadiyah, Herfina, and Harris Effendi Thahar. "KONTRIBUSI KETERAMPILAN MEMBACA PEMAHAMAN TEKS CERPEN TERHADAP KETERAMPILAN MENULIS TEKS CERPEN SISWA KELAS XI SMK NEGERI 9 PADANG." Pendidikan Bahasa Indonesia 8, no. 3 (2019): 119. http://dx.doi.org/10.24036/107468-019883.
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ABSTRACT The purpose of this study was categorized into three. First, to describe the reading skills in understanding the short story of the second-year students at SMK Negeri 9 Padang. Second, to describe the writing skills of a short story for the second year students at SMK Negeri 9 Padang. Third, to describe the contribution of reading short story comprehension into the writing skills of a short story made by the second year students of SMK Negeri 9 Padang. The design of this research was quantitative with a descriptive method. Then, this study also was a correlational design. The population of this study was the second - grade students at SMK Negeri 9 Padang in the academic year 2018/2019 for about 232 students. The sample of this study was taken by using a proportional random sampling technique (20%), which was 46 students. The data of this study were the results of reading skills tests for understanding short story texts and the results of the text writing short story texts. The instrument of this study was an objective test to measure reading skills in understanding short story texts and performance tests to measure short story texts writing skills. There were several results of this study. First, the writing skill of the short story text of the second-year students at SMK Negeri Padang was more than adequate (LdC). Second, reading skills understanding the short text of the second-year students at SMK Negeri 9 Padang was in a Good qualification (B). Third, reading skills understanding the short story of the second-year students at SMK Negeri 9 Padang contributed 72.1% to the short story writing skills of the second year students of SMK Negeri 9 Padang. Kata Kunci: Kontribusi, Keterampilan Membaca Pemahaman Teks Cerpen, Keterampilan Menulis Teks Cerpen
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Lin, Jiayin, Geng Sun, Jun Shen, et al. "From computer vision to short text understanding: Applying similar approaches into different disciplines." Intelligent and Converged Networks 3, no. 2 (2022): 161–72. http://dx.doi.org/10.23919/icn.2022.0010.
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Guan, Jian, Zhuoer Feng, Yamei Chen, et al. "LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation." Transactions of the Association for Computational Linguistics 10 (2022): 434–51. http://dx.doi.org/10.1162/tacl_a_00469.
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Abstract Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing (NLP) usually focus only on understanding or generating short texts. However, long text modeling requires many distinct abilities in contrast to short texts, such as the modeling of long-range discourse and commonsense relations, and the coherence and controllability of generation. The lack of standardized benchmarks makes it difficult to assess these abilities of a model and fairly compare different models, especially Chinese models. Therefore, we propose a story-centric benchmark named LOT for evaluating Chinese long text modeling, which aggregates two understanding tasks and two generation tasks. We construct new datasets for these tasks based on human-written Chinese stories with hundreds of words. Furthermore, we release an encoder-decoder-based Chinese long text pretraining model named LongLM with up to 1 billion parameters. We pretrain LongLM on 120G Chinese novels with two generative tasks including text infilling and conditional continuation. Extensive experiments show that LongLM outperforms similar-sized pretraining models substantially on both the understanding and generation tasks in LOT.
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Sofyaningrum, Rosita. "Decoding “Asap-Asap itu Telah Menghilang”: Understanding Environmental Crisis in Indonesia’s Short Story." Lingual: Journal of Language and Culture 17, no. 1 (2024): 49. http://dx.doi.org/10.24843/ljlc.2024.v17.i01.p06.
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Abstract This research focuses on the analysis of language and narrative in understanding how literary works can contribute to public awareness and understanding of the environmental crisis in Indonesia. It serves as an initial step in comprehending the role of language and literature in depicting the environmental crisis in the country. The study aims to reveal how the use of language, narrative, characters, symbolism, environmental context analysis, and environmental crisis analysis in the short story "The Vanishing Smoke" can provide a deeper understanding of the environmental crisis in Indonesia. The research method employed is an in-depth text analysis to comprehend the short story "The Vanishing Smoke." The methodology includes data collection, text analysis, and contextual analysis. The findings include: (1) Language analysis consisting of diction, language style, symbolism, and elements in the short story. (2) Environmental Context Analysis, including environmental context and environmental crisis. (3) Environmental Crisis Analysis, encompassing the messages in the short story, the environmental issue perspective in depicting the main character's feelings, and the implied messages to the public regarding environmental issues. Keywords: Indonesia’s Short Story, Environmental Crisis
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SRIVASTAVA, B. K., R. P. SCHARENBERG, and T. J. TARNOWSKY. "UNDERSTANDING THE PARTICLE PRODUCTION MECHANISM WITH CORRELATION STUDIES USING LONG AND SHORT RANGE CORRELATIONS." International Journal of Modern Physics E 16, no. 07n08 (2007): 2210–15. http://dx.doi.org/10.1142/s0218301307007702.
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Long range forward-backward multiplicity correlations have been measured with the STAR detector for Au + Au collisions at [Formula: see text]. Strong long range correlations are observed in central Au + Au collisions. Based on the Dual Parton model and Color Glass Condensate considerations the data suggests that these long range correlations are due to multiple parton interactions. This suggests that dense partonic matter is created in central Au + Au collisions at [Formula: see text].
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LI, Zehong, Yuchen ZHAO, and Xu HAN. "Research on Movie Short Reviews Based on Text Analysis." Journalism and Communication Science Bulletin 1, no. 2 (2024): 16–22. https://doi.org/10.48014/jcsb.20240814006.
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Traditional film evaluations often stem from expert reviews or surveys, but they fail to comprehensively reflect the genuine opinions of audiences. The rise of online media and user comments has made web-based text a valuable resource that can assist us in understanding audience evaluations of films and assessing their quality. Taking the film “Spicy and Hot” as an example, this study collected a vast amount of online comment data from 2024. 2. 10 to 2024. 6. 10 and analyzed these comments using natural language processing and text mining techniques based on ROST_CM6 to explore the factors influencing film quality. Firstly, the study evaluated the sentiment polarity in the text to determine the overall emotional orientation of audiences towards the film. Secondly, it analyzed the film characteristics mentioned in the comments, such as plot, actor performance, visual effects, and explored the correlations between these characteristics and film quality. Additionally, this study employed the analytical perspective of the agenda-setting theory to deeply discuss how media influence audience perceptions and evaluation criteria of these topics through reporting strategies, aiming to provide beneficial insights for future film creation and marketing strategies.
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Santos, Diego A. H. Ortega dos, and Claudio E. M. Banzato. "The depressed text." Revista Latinoamericana de Psicopatologia Fundamental 24, no. 1 (2021): 188–99. http://dx.doi.org/10.1590/1415-4714.2021v24n1p188.10.
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North American writer David Foster Wallace wrote two short stories - The Planet Trillaphon As It Stands In Relation To The Bad Thing and The Depressed Person - that depict depression, in each one taking different yet complementary perspectives on this subject. Our aim is to analyze these texts and to discuss the role literature can have in regard to the apprehension of subjective experiences of others. Whereas the first text attempts to describe depression objectively, the second one describes the impossibility of doing so, focusing on literary techniques that create distressing subjective experiences in the reader, possibly resembling those felt by depressed persons. We suggest that literature might be helpful to comprehend some aspects of the experience of being depressed and that such an understanding may enrich psychiatric practice.
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Shi, Jiawen, Hong Li, Chiyu Wang, Zhicheng Pang, and Jiale Zhou. "Pseudo-siamese networks with lexicon for Chinese short text matching." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 6097–109. http://dx.doi.org/10.3233/jifs-202592.
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Short text matching is one of the fundamental technologies in natural language processing. In previous studies, most of the text matching networks are initially designed for English text. The common approach to applying them to Chinese is segmenting each sentence into words, and then taking these words as input. However, this method often results in word segmentation errors. Chinese short text matching faces the challenges of constructing effective features and understanding the semantic relationship between two sentences. In this work, we propose a novel lexicon-based pseudo-siamese model (CL2 N), which can fully mine the information expressed in Chinese text. Instead of utilizing a character-sequence or a single word-sequence, CL2 N augments the text representation with multi-granularity information in characters and lexicons. Additionally, it integrates sentence-level features through single-sentence features as well as interactive features. Experimental studies on two Chinese text matching datasets show that our model has better performance than the state-of-the-art short text matching models, and the proposed method can solve the error propagation problem of Chinese word segmentation. Particularly, the incorporation of single-sentence features and interactive features allows the network to capture the contextual semantics and co-attentive lexical information, which contributes to our best result.
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Butakova, L. O., and E. N. Guts. "Issues of understanding a literary text from a psycholinguistic perspective." Russian language at school 84, no. 6 (2023): 56–68. http://dx.doi.org/10.30515/0131-6141-2023-84-5-56-68.
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The article describes psycholinguistic methods used to model the processes of modern schoolchildren’s perception and comprehension of the text of A. P. Chekhov’s short story "The Death of a Government Clerk". We postulate that the processes of perceiving and understanding fictional texts are conditioned by several factors. These include the quality of the text itself, its size, the closeness of the work to recipients, recipients’ gender- and age-specific qualities, and the peculiarities of their conceptual systems. The research data are the results of two-stage psycholinguistic experiments conducted with the participation of Omsk secondary and vocational school students. The first stage was performed according to the method of semantic differential. It aimed to diagnose the process of emotional perception in the experimental subjects. In the second stage, the students had to answer questions concerning the basic semantic lines of the text. The first stage revealed an emotional attitude to the text and the influence of the gender factor. The former was manifested in different intensities of the evaluation parameters concentrated in the area of negative coefficients. The second stage showed the results of the comprehension process as the totality of gender-, socially, and psychologically determined interpretations of the semantic content of the text. The comprehension process is associated with choosing certain supports such as particular textual signs, the socio-psychological model of the author, the plot, and others.
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Vančo, Ildikó, and Viktória Gergelyová. "Analysis of cognitive operation in understanding text of the fourth-grade pupils." Journal of Linguistics/Jazykovedný casopis 71, no. 3 (2020): 409–24. http://dx.doi.org/10.2478/jazcas-2021-0007.
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Abstract This study aims to analyse the informative-type text, questions, and answers of the fourth-grade reading comprehension test according to cognitive processes. A total of 353 respondents participated in the survey. The examined target group was the fourth-grade pupils of Hungarian-language primary schools in bilingual regions in Eastern, Central, and Western Slovakia. The results obtained show that most of the pupils had sufficient background knowledge to interpret the short and simple text, and the new information was well integrated into their existing schema structure. In terms of processes of comprehension, most pupils had no problem with recognizing and retrieving explicitly stated information in the text, neither with making straightforward inferences. However, there were problems in interpreting and integrating information and summarizing them. The results show that half of the pupils had problems with multi-level interpretation of the information obtained and about one-fifth of the pupils gave incorrect answers even to the questions that required the use of the simplest cognitive processes.
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Martiana, Nita, and Harris Effendi Thahar. "KARAKTERISTIK TEKS CERPEN KARYA SISWA KELAS XI SMA NEGERI 11 PADANG." Pendidikan Bahasa Indonesia 8, no. 5 (2020): 300. http://dx.doi.org/10.24036/108213-019883.
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ABSTRACT The purpose of this study is to describe (1) the characteristics of the short story text structure of class XI MIPA 4 SMAN 11 Padang and (2) the characteristic of the short story text elements by class XI MIPA 4 SMAN 11 Padang. Theories related to this research there are four theories, namely (a) understanding of short story text, (b) short story text structure, (c) elements in short story text, and (d) short story text style. The type of research used is qualitative research using descriptive methods. The data of this study are the results of the writing of the students of class XI MIPA 4 SMAN 11 Padang in the form of short stories totaling 33 short stories. The technique for validating the data used is a thick description. Data were analyzed using simple statistical descriptive techniques and qualitative analysis using formats. Based on the results of the study can be concluded,first, short story text by students of class XI MIPA 4 SMAN 11 Padang are more dominantly built on five structures, namely abstract, orientation, complications, evaluation, and resolution. It can be interpreted that the short story text by students of class XI MIPA 4 SMAN 11 Padang is not complex text structures. Second, short story text by students of class XI MIPA 4 SMAN 11 Padang dominantly using advanced plot. Third, short story text by students of class XI MIPA 4 SMAN 11 Padang then to use three types of language styles, namely hyperbolic style, personification, and simile. Kata kunci : Karakteristik, teks cerpen
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Satriawan, Rahmad, Irfani Basri, and Abdurrahman Abdurrahman. "KORELASI KETERAMPILAN MEMBACA PEMAHAMAN CERPEN DENGAN KETERAMPILAN MENULIS TEKS ULASAN CERPEN SISWA KELAS VIII SMP NEGERI 1 PADANG." Pendidikan Bahasa Indonesia 9, no. 1 (2020): 160. http://dx.doi.org/10.24036/108277-019883.
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ABSTRACT The purpose of this study consists of three things as follows. First, it describes the level of reading skills in understanding the short stories of students of class VIII SMP Negeri 1 Padang. Secondly, it describes the level of short story writing skills of students of VIII SMP Negeri 1 Padang. Third, describe the relationship between reading skills understanding short stories and short story writing skills of students of VIII SMP Negeri 1 Padang. The variables of this research are the short story comprehension reading skills as the X variable and the short story writing text writing skills as the Y variable. The research data were the scores of the short story reading comprehension test skills of the eighth grade students of SMP Negeri 1 Padang and the scores of the results of the short story writing skills text review of the eighth grade students of SMP Negeri 1 Padang. The results of this study consisted of three things as follows. First, the reading skills of the short story comprehension of Grade VIII students of SMP Negeri 1 Padang are in the Qualification More Than Enough with an average count of 67.01. Second, the short story writing skills of the eighth grade students of SMP Negeri 1 Padang in short story writing are in the Fair qualifications with an average count of 62.15. Third, based on the results of the t-test, an alternative hypothesis was accepted at the 95% confidence level. Kata Kunci: Korelasi, Membaca Pemahaman Cerpen, Menulis Teks Ulasan Cerpen
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Liu, Yi, Yue Zhang, Haidong Hu, Xiaodong Liu, Lun Zhang, and Ruijun Liu. "An Extended Text Combination Classification Model for Short Video Based on Albert." Journal of Sensors 2021 (October 16, 2021): 1–7. http://dx.doi.org/10.1155/2021/8013337.
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With the rise and rapid development of short video sharing websites, the number of short videos on the Internet has been growing explosively. The organization and classification of short videos have become the basis for the effective use of short videos, which is also a problem faced by major short video platforms. Aiming at the characteristics of complex short video content categories and rich extended text information, this paper uses methods in the text classification field to solve the short video classification problem. Compared with the traditional way of classifying and understanding short video key frames, this method has the characteristics of lower computational cost, more accurate classification results, and easier application. This paper proposes a text classification model based on the attention mechanism of multitext embedding short video extension. The experiment first uses the training language model Albert to extract sentence-level vectors and then uses the attention mechanism to study the text information in various short video extensions in a short video classification weight factor. And this research applied Google’s unsupervised data augmentation (UDA) method based on unsupervised data, creatively combining it with the Chinese knowledge graph, and realized TF-IDF word replacement. During the training process, we introduced a large amount of unlabeled data, which significantly improved the accuracy of model classification. The final series of related experiments is aimed at comparing with the existing short video title classification methods, classification methods based on video key frames, and hybrid methods, and proving that the method proposed in this article is more accurate and robust on the test set.
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Besamusca, Bart, Gareth Griffith, Matthias Meyer, and Hannah Morcos. "Author Attributions in Medieval Text Collections: An Exploration." Amsterdamer Beiträge zur älteren Germanistik 76, no. 1 (2016): 89–122. http://dx.doi.org/10.1163/18756719-12340004.
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This article examines the role and function of author attributions in multi-text manuscripts containing Dutch, English, French or German short verse narratives. The findings represent one strand of the investigations undertaken by the cross-European project ‘The Dynamics of the Medieval Manuscript’, which analysed the dissemination of short verse narratives and the principles of organisation underlying the compilation of text collections. Whilst short verse narratives are more commonly disseminated anonymously, there are manuscripts in which authorship is repeatedly attributed to a text or corpus. Through six case studies, this article explores medieval concepts of authorship and how they relate to constructions of authority, whether regarding an empirical figure or a literary construction. In addition, it looks at how authorship plays a role in manuscript compilation, and at the effects of attributions (by author and/or compiler) on reception. The case studies include manuscripts from the thirteenth to fifteenth centuries, produced in a range of social and cultural contexts, and featuring some of the most important European authors of short verse narratives: Rutebeuf, Baudouin de Condé, Der Stricker, Konrad von Würzburg, Willem of Hildegaersberch, and Geoffrey Chaucer. The preliminary findings contribute to our understanding of author attributions in text collections from across northern Europe and point towards future lines of enquiry into the role of authorship in medieval textual dissemination.
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Apoorva, R. Prakash, Vishwa Karma Deepshikha, K. Krithika, Kumar Sahu Rajesh, and NR Dr.Deepak. "Understanding human emotions: Advances in Sentiment Analysis." Journal of Research and Reviews in Human Computer Interaction 1, no. 1 (2025): 10–14. https://doi.org/10.5281/zenodo.14929552.
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<em>“Sentiment analysis”, a crucial part of “natural language processing (NLP)”, it includes considering people’s emotions and feelings on a subject from text to determine the writer's emotional tone. This paper tries to understand different methods and models for sentiment analysis, including traditional approaches such as rule-based systems and machine learning classifiers, as well as techniques for “deep learning” like “Recurrent Neural Networks (RNNs)”, “Long Short-Term Memory (LSTM)”, and “Transformers”. By comparing these techniques, we analyse their effectiveness across different datasets and languages. The study highlights key challenges such as sarcasm detection, context understanding, and handling demonstrate the effect of pre-trained language models, especially BERT and GPT, in improving sentiment classification accuracy. The paper concludes by discussing potential applications in analysing the feebacks given by customers, and ‘social media’ monitoring, and decision-making processes</em>
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WELLNER, BEN, LISA FERRO, WARREN GREIFF, and LYNETTE HIRSCHMAN. "Reading comprehension tests for computer-based understanding evaluation." Natural Language Engineering 12, no. 4 (2005): 305–34. http://dx.doi.org/10.1017/s1351324905004018.
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Reading comprehension (RC) tests involve reading a short passage of text and answering a series of questions pertaining to that text. We present a methodology for evaluation of the application of modern natural language technologies to the task of responding to RC tests. Our work is based on ABCs (Abduction Based Comprehension system), an automated system for taking tests requiring short answer phrases as responses. A central goal of ABCs is to serve as a testbed for understanding the role that various linguistic components play in responding to reading comprehension questions. The heart of ABCs is an abductive inference engine that provides three key capabilities: (1) first-order logical representation of relations between entities and events in the text and rules to perform inference over such relations, (2) graceful degradation due to the inclusion of abduction in the reasoning engine, which avoids the brittleness that can be problematic in knowledge representation and reasoning systems and (3) system transparency such that the types of abductive inferences made over an entire corpus provide cues as to where the system is performing poorly and indications as to where existing knowledge is inaccurate or new knowledge is required. ABCs, with certain sub-components not yet automated, finds the correct answer phrase nearly 35 percent of the time using a strict evaluation metric and 45 percent of the time using a looser inexact metric on held out evaluation data. Performance varied for the different question types, ranging from over 50 percent on who questions to over 10 percent on what questions. We present analysis of the roles of individual components and analysis of the impact of various characteristics of the abductive proof procedure on overall system performance.
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Hu, Qiong. "A cross-language short text classification model based on BERT and multilayer collaborative convolutional neural network (MCNN)." Molecular & Cellular Biomechanics 21, no. 3 (2024): 739. http://dx.doi.org/10.62617/mcb739.
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This study focuses on cross-lingual short text classification tasks and aims to combine the advantages of BERT and Multi-layer Collaborative Convolutional Neural Network (MCNN) to build an efficient classification model. BERT model provides rich semantic information for text classification with its powerful language understanding and bidirectional context modeling ability, while MCNN effectively extracts local and global features in text through multi-layer convolution structure and collaborative working mechanism. In this study, the output of BERT is used as the input of MCNN, and MCNN is used to further mine the deep features in the text, so as to realize the high-precision classification of cross-lingual short text. The experimental results show that the model has achieved significant performance improvement on the dataset, which provides a new effective solution for cross-lingual short text classification tasks.
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Melhem, Wasen Yahya, Asad Abdi, and Farid Meziane. "Deep Learning Classification of Traffic-Related Tweets: An Advanced Framework Using Deep Learning for Contextual Understanding and Traffic-Related Short Text Classification." Applied Sciences 14, no. 23 (2024): 11009. http://dx.doi.org/10.3390/app142311009.
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Classifying social media (SM) messages into relevant or irrelevant categories is challenging due to data sparsity, imbalance, and ambiguity. This study aims to improve Intelligent Transport Systems (ITS) by enhancing short text classification of traffic-related SM data. Deep learning methods such as RNNs, CNNs, and BERT are effective at capturing context, but they can be computationally expensive, struggle with very short texts, and perform poorly with rare words. On the other hand, transfer learning leverages pre-trained knowledge but may be biased towards the pre-training domain. To address these challenges, we propose DLCTC, a novel system combining character-level, word-level, and context features with BiLSTM and TextCNN-based attention. By utilizing external knowledge, DLCTC ensures an accurate understanding of concepts and abbreviations in traffic-related short texts. BiLSTM captures context and term correlations; TextCNN captures local patterns. Multi-level attention focuses on important features across character, word, and concept levels. Experimental studies demonstrate DLCTC’s effectiveness over well-known short-text classification approaches based on CNN, RNN, and BERT.
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Harun, Lisnawati. "The Implementation of Learning Model in Talking Stick by Using Short Story Text to Improve Students' Reading Comprehension At SMP Negeri 15 TIKEP." Langua: Journal of Linguistics, Literature, and Language Education 1, no. 1 (2018): 49–61. https://doi.org/10.5281/zenodo.1412297.
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This Research is a class act that uses short story text thorough learning model of talking stick to improve students’ reading comprehension. This subject is class VII SMP NEGERI 15 Tidore Kepulauan 2017-2018 school year the sample of 12 students. This study to be implemented in one class, each cycle consisting of four components, namely: planning, implementation, observation, and reflection. Data were collected through taking video, observation sheet and field note on each cycle. Based on the results of research, teaching by using short story text through learning model of talking stick can improving students’ reading comprehension of the students class VII SMP NEGERI 15 TIKEP. The results of the research subjects to study of reading and understanding the meaning of short story text on the actions of the first cycle still there are 6 students who do not achieve mastery, while 6 other students have achieved mastery. In the second cycle action, and from 12 students who following the learning, all of them achieved mastery. Which means all of students in the second cycle have completed if the value measured by KKM 70.
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Zhang, Jianming. "Weibo Text Sentiment Classification Model Based on FastText-BERT-Attention." Journal of Big Data and Computing 2, no. 1 (2024): 29–35. http://dx.doi.org/10.62517/jbdc.202401104.
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This paper introduces a Weibo text sentiment classification model that integrates FastText, BERT, and an attention mechanism, aiming to overcome the limitations of traditional models in processing social media data. By leveraging FastText's efficient word-level feature extraction capabilities, BERT's deep semantic understanding, and the feature fusion advantage of the attention mechanism, this model significantly enhances the accuracy of Weibo text classification. Experimental results show that compared to Word2Vec, FastText, and BERT models, the FastText-BERT-Attention model proposed in this paper demonstrates higher precision, recall, and F1 scores in the sentiment binary classification task, proving its effectiveness and superiority in handling large-scale short text data from Weibo comments. This study not only presents theoretical innovation but also exhibits superior performance in practical applications, making it particularly suitable for processing short text data from social media platforms like Weibo.
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Massey, L. "A Cognitive Framework for Core Language Understanding and its Computational Implementation." International Journal of Cognitive Informatics and Natural Intelligence 6, no. 1 (2012): 1–20. http://dx.doi.org/10.4018/jcini.2012010101.
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The author argues that the cognitive processes underlying language understanding may not be logico-deductive or inductive, at least not for basic forms of understanding such as the ability to determine the topics of a text document. To demonstrate this point, they present a human cognition inspired framework for core language understanding and its computational implementation. The framework exploits word related knowledge stored in Long Term Memory (LTM) as well as Short Term Memory (STM) limited capacity, neuromorphic spreading activation and neural activation decay to derive the topics of text. The computational model implementing the framework shows the potential of the approach by establishing that the topics generated by the model are as good as those generated by humans.
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Frye, Joanne S. "Tillie Olsen: Probing the Boundaries Between Text and Context." Keeping Ourselves Alive 3, no. 2-3 (1993): 255–68. http://dx.doi.org/10.1075/jnlh.3.2-3.10til.
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Abstract Cultural criticism necessitates not only an examination of the context of the writer but also a broader understanding of the ways in which text and context are integrally interrelated. For pursuing these intersections, Tillie Olsen's short fiction in Tell Me a Riddle is particularly exemplary because of its textual richness and its distinctive ways of drawing on historical context. Interviews with Olsen heighten the significance of her particular context in the 1950s and emphasize the shaping effect that circumstances had on her choices in language and form as she wrote this fiction. Two concerns surface as particularly impor-tant: family life and political activism. An inquiry into these two concerns then suggests the complexity of how voice and circumstance, language and social forces, interact—in the writer, in the text, in the reader, and finally in the choices we might make for shaping alternative understandings of cultural change. (Cultural criticism; literary criticism)
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Fialho, Olívia da Costa. "Foregrounding and refamiliarization: understanding readers' response to literary texts." Language and Literature: International Journal of Stylistics 16, no. 2 (2007): 105–23. http://dx.doi.org/10.1177/0963947007075979.
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The present study investigates the effects of foregrounding on the process of defamiliarization of students of literature and engineering, and on the way they develop refamiliarization, that is, the reconstructive process they undergo in order to return to familiar ground. It describes which refamiliarizing strategies these readers make use of and the role of feeling in this process. Data analysis is both quantitative and qualitative. The introspective method of the pause protocol is used in the qualitative part. Here, participants respond to the reading of a short story. The purpose is to investigate how they react to its content and which of its segments trigger comments. Results demonstrate that appreciating the formal elements of a text might be an effective strategy, as readers do not try to decode the text any longer and start reflecting on it, thus building an interpretation. They also develop a new perspective on the world around them and on themselves.
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Newell, George E., Karen Suszynski, and Ruth Weingart. "The Effects of Writing in a Reader-Based and Text-Based Mode on Students' Understanding of Two Short Stories." Journal of Reading Behavior 21, no. 1 (1989): 37–57. http://dx.doi.org/10.1080/10862968909547657.
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This study examined how personal versus formal writing tasks affect what students take from literary text. The writing samples produced by sixty-five 10th-grade students in response to two short stories were analyzed for quality of response, audience, function, syntactic complexity, fluency, and types of response statements. Findings indicated that the reader-based or personal writing tasks enabled the students to produce qualitatively more effective responses that tended to be more fluent and constructed with a wider range of response statements. A shift in audience from teacher-as-examiner to teacher-student dialogue in the personal writing indicated a tentativeness that permitted the students to invite their reader into their explorations of the short stories.
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Xie, Tianbao, Yuqi Han, Ganglong Duan, Siyu Yang, Shaoyang Zhang, and Yongcheng Shao. "Section Recommendation of Online Medical Platform Based on Keyword Expansion with Self-Adaptive-Attention-Prompt-BERT-RCNN Modeling." Applied Sciences 15, no. 12 (2025): 6746. https://doi.org/10.3390/app15126746.
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Background: Implementing automatic classification of short texts in online healthcare platforms is crucial to increase the efficiency of their services and improve the user experience. A short text classification method combining the keyword expansion technique and a deep learning model is constructed to solve the problems of feature sparsity and semantic ambiguity in short text classification. Methods: First, we use web crawlers to obtain patient data from the online medical platform “Good Doctor”; then, we use TF-IWF to weight the keyword importance and Word2vec to calculate the keyword similarity to expand the short text features; and then we integrate the cue learning and deep learning models to construct a self-adaptive attention model to solve the problem of sparse features and unclear semantics in short text classification in the adaptive-attention-Prompt-BERT-RCNN model to realize effective classification of medical short texts. Results: Empirical studies show that the classification effect after keyword expansion is significantly higher than that before expansion, the accuracy of the model in classifying medical short texts after expansion is as high as 97.84%, and the model performs well in different categories of medical short texts. Conclusions: The short text expansion methods of TF-IWF and Word2vec make up for the shortcomings of not taking into account the keyword rarity and the contextual information of the subwords, and the model can achieve effective classification of medical short texts by combining them. The model further improves the classification accuracy of short text by integrating Prompt’s bootstrapping, self-adaptive attention’s keyword weight weighting, BERT’s deep semantic understanding, and RCNN’s region awareness and feature extraction; however, the model’s accuracy in individual topics still needs to be improved. The results show that the recommender system can effectively improve the efficiency of patient consultation and support the development of online healthcare.
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Somers, Rick, Samuel Cunningham-Nelson, and Wageeh Boles. "Applying natural language processing to automatically assess student conceptual understanding from textual responses." Australasian Journal of Educational Technology 37, no. 5 (2021): 98–115. http://dx.doi.org/10.14742/ajet.7121.
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In this study, we applied natural language processing (NLP) techniques, within an educational environment, to evaluate their usefulness for automated assessment of students’ conceptual understanding from their short answer responses. Assessing understanding provides insight into and feedback on students’ conceptual understanding, which is often overlooked in automated grading. Students and educators benefit from automated formative assessment, especially in online education and large cohorts, by providing insights into conceptual understanding as and when required. We selected the ELECTRA-small, RoBERTa-base, XLNet-base and ALBERT-base-v2 NLP machine learning models to determine the free-text validity of students’ justification and the level of confidence in their responses. These two pieces of information provide key insights into students’ conceptual understanding and the nature of their understanding. We developed a free-text validity ensemble using high performance NLP models to assess the validity of students’ justification with accuracies ranging from 91.46% to 98.66%. In addition, we proposed a general, non-question-specific confidence-in-response model that can categorise a response as high or low confidence with accuracies ranging from 93.07% to 99.46%. With the strong performance of these models being applicable to small data sets, there is a great opportunity for educators to implement these techniques within their own classes. Implications for practice or policy: Students’ conceptual understanding can be accurately and automatically extracted from their short answer responses using NLP to assess the level and nature of their understanding. Educators and students can receive feedback on conceptual understanding as and when required through the automated assessment of conceptual understanding, without the overhead of traditional formative assessment. Educators can implement accurate automated assessment of conceptual understanding models with fewer than 100 student responses for their short response questions.
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Донець, С. М. "ЗАГОЛОВОК ЯК ЕЛЕМЕНТ СИЛЬНОЇ ПОЗИЦІЇ У ОПОВІДАННЯХ С. МОЕМА". Наукові записки Харківського національного педагогічного університету ім. Г. С. Сковороди "Літературознавство" 3, № 93 (2019): 54–66. http://dx.doi.org/10.34142/2312-1076.2019.3.93.05.
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The paper deals with functioning of strong position elements, particularly, the title in short stories of the English fiction. The analysis is based on 60 short stories of a famous English writer S. Maugham. Strong position is understood as completeness of a fictional text in the title, epigraph, beginning and the ending, organizing the decoding strategy and facilitating the perception of the main idea of the text. Different approaches to the issue of strong position (as a type of foregrounding or actualization in the decoding stylistics or as a frame element in literature studies) are considered. An attempt is made to classify the titles of the author’s short stories as the title-symbol, the title-thesis, the title-citation, the title-message, the title-narration. A more detailed analysis of the short story «Rain» revealed that the foregrounding elements have a symbolic character, determine the interrelations between the text fragments and provide for a holistic concept of the text. The title-symbol «Rain» becomes the key image of the work. Constant repetition of the key word «rain», its strong position as a title extends the meaning of the word to the symbolic meaning, creates the imagery perspective of the story. The word itself undergoes semantic changes resulting in a new individual artistic meaning which is realized retrospectively. The analysis of peculiarities of strong position elements proved that one of their main functions is foregrounding the most important information. The title has a decisive position in the content structure: it performs nominative, informative, communicative, expressive-appealing, delimiting, symbolic, evaluative functions. Consideration of the elements of strong position and, primarily, the title enhances the decoding of structural integrity and completeness of the text, understanding of its deep level meaning encoded in the title.
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Defianty, Rina, Afril Lita Karyani, Farhanasyah Putri, Sri Handayani, Trinata Asmara, and Suryani Suryani. "CRITICAL DISCOURSE ANALYSIS OF SARA MILLS MODEL ON THE SHORT STORY “SI MONTOK” BY A.A NAVIS." LUMBUNG AKSARA 5, no. 1 (2025): 27–33. https://doi.org/10.47662/lumra.v5i1.1105.
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This study analyzes the short story ‘Si Montok’ by A.A. Navis using Sara Mills’ critical discourse analysis model, focusing on the representation of women in the text and how the text reconstructs power relations in society. This analysis aims to reveal how the discourse in the short story presents and perhaps challenges the construction of gender and power in society. Through the analysis of the positions of the subject, object, and reader in the narrative structure, this study finds that the short story ‘Si Montok’ tends to represent women from a male perspective, objectifies women’s bodies, and reinforces existing social stereotypes of women. This analysis is expected to enrich Indonesian literary studies with a critical discourse analysis perspective, especially Sara Mills’ model, which emphasizes the importance of social and political context in understanding the text.
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Shilpa, Shilpa, and Soma Shridevi. "Enhancing text classification through novel deep learning sequential attention fusion architecture." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4642–53. https://doi.org/10.11591/ijai.v13.i4.pp4642-4653.
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Text classification is a pivotal task within natural language processing (NLP), aimed at assigning semantic labels to text sequences. Traditional methods of text representation often fall short in capturing intricacies in contextual information, relying heavily on manual feature extraction. To overcome these limitations, this research work presents the sequential attention fusion architecture (SAFA) to enhance the features extraction. SAFA combines deep long sort-term memory (LSTM) and multi-head attention mechanism (MHAM). This model efficiently preserves data, even for longer phrases, while enhancing local attribute understanding. Additionally, we introduce a unique attention mechanism that optimizes data preservation, a crucial element in text classification. The paper also outlines a comprehensive framework, incorporating convolutional layers and pooling techniques, designed to improve feature representation and enhance classification accuracy. The model's effectiveness is demonstrated through 2-dimensional convolution processes and advanced pooling, significantly improving prediction accuracy. This research not only contributes to the development of more accurate text classification models but also underscores the growing importance of NLP techniques.
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Mounguengui, Faustin, and Samuel Nyock Ilouga. "Illustration and Text Comprehension: Tales Study for Primary Students." Journal of Educational and Developmental Psychology 9, no. 1 (2019): 90. http://dx.doi.org/10.5539/jedp.v9n1p90.
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Essential for the individual&rsquo;s successful adaptation in the society, text comprehension is a cognitive activity more and more complicated to the child (Blanc &amp; Brouillet, 2005). However, these understanding difficulties get better with the age (Boisclair, Makdissi, Sanchez, Fortier, &amp; Sirois, 2004; Segui &amp; L&eacute;veill&eacute;, 1977). The present study, working on the principle that illustration is beneficial to the comprehension process (Ammari, 2015; Blanc &amp; Tapiero, 2002; Gyselinck, 1995, 1996; Gyselinck &amp; Tardieu, 1993; Reinwein, 1988; Rizk Batien, 2009; Vezin, 1986), has proposed to study the effect, the role of this one on comprehension to the children through tales. Two groups of students aged from 7 to 9 years old read two short stories, illustrated or not. Two tests were also submitted to them: the task of understanding and highlighting important words and/or ideas. The results obtained show clearly the positive effect of the illustration on the students&rsquo; performances and testify thus to the fact that illustration can be used as a palliative to the understanding difficulties of to the children.
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Al – Azzawi, Istabraq Tariq, and Ibraheem Khalaf Salih Al-Jubouri. "The Importance of Figurative Speech in Teaching Short Story." Journal of Tikrit University for Humanities 29, no. 7, 2 (2022): 1–30. http://dx.doi.org/10.25130/jtuh.29.7.2.2022.24.
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Figurative language is significant in the comprehension of any literary text. It affects the understanding of each idea that it reflects. This study aims to:
 
 Show if there is a difficulty in figurative language through studying short story.
 State which kind of figurative language causes difficulty for EFL University students in studying short story.
 
 The sample of this study is 93 students at the second grade of English Department at Tikrit University in the academic year 2020/2021. The data is gathered using an achievement test to determine students' short story achievement. The test includes two questions with 10 items for each.
 Finally, to analyze the obtained data, suitable statistical methods are used to analyze the results of the test. The results revealed that the majority of students face some difficulties in using figurative language through studying short story and metaphor and hyperbole are the most difficult rather than the others.
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Inupakutika, D., M. Nadim, G. R. Gunnam, et al. "Integration of NLP and Speech-to-text Applications with Chatbots." Electronic Imaging 2021, no. 3 (2021): 35–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.3.mobmu-035.
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With the evolving artificial intelligence technology, the chatbots are becoming smarter and faster lately. Chatbots are typically available round the clock providing continuous support and services. A chatbot or a conversational agent is a program or software that can communicate using natural language with humans. The challenge of developing an intelligent chatbot still exists ever since the onset of artificial intelligence. The functionality of chatbots can range from business oriented short conversations to healthcare intervention based longer conversations. However, the primary role that the chatbots have to play is in understanding human utterances in order to respond appropriately. To that end, there is an increased emergence of Natural Language Understanding (NLU) engines by popular cloud service providers. The NLU services identify entities and intents from the user utterances provided as input. Thus, in order to integrate such understanding to a chatbot, this paper presents a study on existing major NLU platforms. Then, we present a case study chatbot integrated with Google DialogFlow and IBM Watson NLU services and discuss their intent recognition performance.
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Krastanova, Dorotea. "Functions of Eventfulness in Svetoslav Minkov’s Short Stories." Филологически форум, no. 19 (2024): 107–17. http://dx.doi.org/10.60056/philolf.2024.1.107-117.
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The starting point of this paper is understanding the event as a part which organizes the totality of a narrative. The object of examination are the short stories by Svetoslav Minkov ‘Monkey’s Youth’ („Маймунска младост“), ‘The Man Who Came from America’ („Човекът, който дойде от Америка“), ‘The Hydrogenous Man and the Oxygenous Girl’ („Водородният господин и кислородното момиче“) and ‘Why I Turned Out to Be without a Wraith’ („Защо останах без двойник“). In their analyses emphasis is placed on eventfulness. The text gives a definition of an event and aims to track and analyze the different transformations in short stories. The thesis of the paper is the refunctionalization of the text’s narrative structure, which is achieved through change in discourse at the moment of an event taking place. The text represents an attempt to outline the most obvious relations between the event, plot and the way they affect the individual and their sense. of time.
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Haroutyunian, Sona, and Amalia Haroutyunian. "LINGUOPOETICS OF EPITHET IN O. HENRY’S SHORT STORIES." Armenian Folia Anglistika 18, no. 2 (26) (2022): 97–107. http://dx.doi.org/10.46991/afa/2022.18.2.097.
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It is common knowledge that reading literature in the proper sense of the word is rather difficult as it presupposes the ability of the reader not to confine one’s perception to what can be seen on the surface of the text only. The reader’s ability to go deep into the text and reveal the intention of the writer is of prime importance. Many prominent philologists both in our country and abroad have referred to this question, and their investigations have established that the real understanding of a piece of verbal creativity is a step by step process and can be achieved only gradually. This, in fact, is the underlying methodological bases of the present research which aims at studying the use of epithets in O’Henry’s short stories and their role in the linguopoetic organization of the stories. Of particular investigative interest have turned out to be The Last Leaf and The Gift of Magi.
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Puspitaning Padmasari, Arumtyas, Aqiel Ajiz Alfaris, Renda Aranggraeni, et al. "Enhancing anti-corruption character through VBL on response text: A case study." Research and Development in Education (RaDEn) 5, no. 1 (2025): 388–402. https://doi.org/10.22219/raden.v5i1.40489.
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Corruption poses a significant challenge in Indonesia, adversely affecting various sectors, including the economy, politics, education, and society at large. Corruption has become ingrained as a culture, character, and mindset within Indonesian society, a phenomenon inherited since the colonial era. This study investigates the effectiveness of video-based learning through short films in enhancing response text about anti-corruption character education for 7th-grade junior high school students in Sidoarjo. It also aims to analyze the impact of the video-based learning method on students' understanding of anti-corruption values and to identify the supporting and inhibiting factors for its implementation. The qualitative research employs a case study approach, focusing on 30 students writing in Response text after watching the short film "RAPOR". This research uses observation, interview, survey, and supporting document for analysing research data. Using categoriation table and graphic are helping the research validation. Findings indicate that video-based learning significantly improves students' understanding of response text which mentions and argues anti-corruption values, enhances critical thinking skills, and fosters discussion engagement. Key supporting factors include the interactive nature of short films, which makes moral lessons more relatable while inhibiting factors encompass limited access to quality educational films and the need for adequate time and resources for analysis. Overall, the study underscores the potential of video-based methods to effectively instill integrity-driven values in the younger generation, thereby contributing to the long-term goal of reducing corruption in Indonesia. Recommendations for educational strategies to improve access and implementation of video-based learning are provided to maximize its impact on character education.
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Wigraha, Ketut Agung Devara. "Reading Difficulty Viewed from Its Indicators in the Junior High School." Jurnal Pendidikan Bahasa Inggris undiksha 10, no. 1 (2022): 60–66. http://dx.doi.org/10.23887/jpbi.v10i1.45157.
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This research aimed to analyze students' reading difficulties in understanding the descriptive text. This research applied descriptive qualitative research. The objects in this research were students' difficulties in the learning process in reading instruction of descriptive text, especially in finding the main idea or central theme, locating specific information, determining textual reference, and getting the meaning of a word or phrase. Students' sources of difficulties derived from the preliminary test results in understanding different descriptive texts, especially the main idea or central theme, specific information, textual reference, and word or phrase referential meaning of descriptive texts of a person, an animal, and a procedure. The subjects of this research were the seven grade students, consisting of 22 students in a class, and documents of students' test results were from difficulties in understanding descriptive texts of a person, an animal, and a procedure. The data were collected in the forms of primary and secondary data. The instruments used by the researcher to collect the data were the researcher as the main instrument, a non-objective test or short answer test consisting of 30 short answer questions related to descriptive texts of a person, an animal, and a procedure, and a focused group interview guide. The obtained data were analyzed descriptively. The results of this research show that students had difficulties in reading three descriptive texts based on reading indicators such as deducing the main idea or central theme, locating specific information, textual reference, and getting a word or phrase meaning. The sources of reading difficulties were identified by the researcher in an interview session with 5 representative students, namely having a limited vocabulary, lack of reading interest, lack of background knowledge or absence of knowledge, and the complexity of words or sentences in the text.
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Patil, Deepika, Aniket Patil, Akshay Patil, Amol More, and Suraj Shinde Patil. "Sign Language to Text Conversion." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 2613–18. http://dx.doi.org/10.22214/ijraset.2024.62147.
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Abstract: Sign language, being one of the oldest and most natural forms of communication, serves as a crucial means of expression for individuals with hearing and speech impairments. Deaf and dumb individuals heavily rely on sign language for communication, given their limitations in using spoken languages. In this context, we are introducing a real-time method utilizing neural networks for finger spelling based on American Sign Language (ASL). Automatic human gesture recognition, especially from camera images, has become an intriguing area for developing computer vision applications. Recognizing hand gestures in real-time from camera images can significantly enhance communication for individuals with hearing and speech impairments. The proposed method employs Long Short-Term Memory (LSTM) to recognize hand gestures associated with American Sign Language. Sign language, being one of the oldest and most natural forms of communication, serves as a crucial means of expression for individuals with hearing and speech impairments. Deaf and Hard-of-Hearing (D&H) individuals heavily rely on sign language for communication, given their limitations in using spoken languages. In this context, we are introducing a real-time method utilizing neural networks for finger spelling based on American Sign Language (ASL). Automatic human gesture recognition, especially from camera images, has become an intriguing area for developing computer vision applications. Recognizing hand gestures in real-time from camera images can significantly enhance communication for individuals with hearing and speech impairments. The proposed method employs Long Short-Term Memory (LSTM) to recognize hand gestures associated with American Sign Language. However, the lack of tools that seamlessly connect sign language with spoken language creates barriers in understanding and interaction. The project aims to break down these barriers by providing a real-time solution for the recognition and translation of finger-spelling-based hand gestures
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Коробова, Анастасия Владиславовна. "UNDERSTANDING OF LITERARY TEXT IN THE CONCEPT OF G.I. BOGIN (BASED ON MATERIALS OF «A PERFECT DAY FOR BANANAFISH» BY J. SALINGER)." Вестник Тверского государственного университета. Серия: Филология, no. 4(71) (December 3, 2021): 231–37. http://dx.doi.org/10.26456/vtfilol/2021.4.231.
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В данной статье интерпретация художественного текста рассматривается сквозь призму герменевтической концепции Георгия Исаевича Богина. Материалом для интерпретации послужил текст произведения Дж. Сэлинджера «A Perfect Day for Bananafish». In this article, the interpretation of a literary text is examined through the prism of the hermeneutic concept of Georgy Isaevich Bogin. The material for the analysis is the text of the short story «A Perfect Day for Bananafish» by J. Salinger.
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Setyanto, Arief, Arif Laksito, Fawaz Alarfaj, et al. "Arabic Language Opinion Mining Based on Long Short-Term Memory (LSTM)." Applied Sciences 12, no. 9 (2022): 4140. http://dx.doi.org/10.3390/app12094140.
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Arabic is one of the official languages recognized by the United Nations (UN) and is widely used in the middle east, and parts of Asia, Africa, and other countries. Social media activity currently dominates the textual communication on the Internet and potentially represents people’s views about specific issues. Opinion mining is an important task for understanding public opinion polarity towards an issue. Understanding public opinion leads to better decisions in many fields, such as public services and business. Language background plays a vital role in understanding opinion polarity. Variation is not only due to the vocabulary but also cultural background. The sentence is a time series signal; therefore, sequence gives a significant correlation to the meaning of the text. A recurrent neural network (RNN) is a variant of deep learning where the sequence is considered. Long short-term memory (LSTM) is an implementation of RNN with a particular gate to keep or ignore specific word signals during a sequence of inputs. Text is unstructured data, and it cannot be processed further by a machine unless an algorithm transforms the representation into a readable machine learning format as a vector of numerical values. Transformation algorithms range from the Term Frequency–Inverse Document Frequency (TF-IDF) transform to advanced word embedding. Word embedding methods include GloVe, word2vec, BERT, and fastText. This research experimented with those algorithms to perform vector transformation of the Arabic text dataset. This study implements and compares the GloVe and fastText word embedding algorithms and long short-term memory (LSTM) implemented in single-, double-, and triple-layer architectures. Finally, this research compares their accuracy for opinion mining on an Arabic dataset. It evaluates the proposed algorithm with the ASAD dataset of 55,000 annotated tweets in three classes. The dataset was augmented to achieve equal proportions of positive, negative, and neutral classes. According to the evaluation results, the triple-layer LSTM with fastText word embedding achieved the best testing accuracy, at 90.9%, surpassing all other experimental scenarios.
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Florit, Elena, Kate Cain, and Maria Chiara Levorato. "Understanding the semantic functions of but in middle childhood: The role of text- and sentence-level comprehension abilities." First Language 37, no. 2 (2016): 109–29. http://dx.doi.org/10.1177/0142723716678383.
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This study examined Italian 7- to 9-year-olds’ understanding of the connective but when used to relate two events in sentences embedded in short stories. Performance was largely accounted for by the cognitive complexity of the sentence that included the connective and the salience of its meaning (confirmed in a second study with adults). Additional influences on children’s performance were the category of the story in which the critical sentence was embedded and the child’s text comprehension abilities. Further, by 9 years of age, performance resembled that of adults. These findings make an advance in explaining the role of information presented in a text at different levels and an individual’s linguistic abilities in children’s understanding of the connective but in stories and its development.