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Journal articles on the topic 'Computer Lexikon'

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Published: 4 June 2021

Last updated: 28 July 2025

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1

Pollard, David. "Lexikon, version 1.2." ReCALL 3, no. 4 (1991): 38–39. http://dx.doi.org/10.1017/s0958344000002585.

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Hearon, Jim. "Lexikon Musikautomaten: Die Welt der selbstspielenden Musikinstrumente." Computer Music Journal 29, no. 1 (2005): 100–101. http://dx.doi.org/10.1162/comj.2005.29.1.100.

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Siegel, Steffen. "Spargel, Pumps und Brooklyn Bridge. Zur Zukunft des vergleichenden Sehens." Zeitschrift für Ideengeschichte 10, no. 2 (2016): 25–40. http://dx.doi.org/10.17104/1863-8937-2016-2-25.

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Zu einer Geschichte «neuer Medien» gehört immer auch eine Geschichte der «nicht mehr ganz so neuen Medien». Diese älteren aber verschwinden nicht einfach hinter den jüngeren, sie stellen vielmehr Fragen ganz eigener Art. Nur zwei Beispiele: Wie soll man umgehen mit all den Text- und Bildmedien, die gerade noch im Zentrum unseres Gebrauchs standen, nun aber einen so dramatischen Kursverfall erleiden? Wer läuft noch ans Regal zu einem alten Lexikon, um nachzuschlagen, was sich vom Schreibtisch aus auch im Internet finden lässt? Und wer macht sich die Mühe, in einer Diathek als Kleinbild-Dia hera

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Strobel, Janina. "Juki – Eine neue Video-Plattform für Kinder geht online." merz | medien + erziehung 57, no. 3 (2013): 3–4. http://dx.doi.org/10.21240/merz/2013.3.3.

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Das Deutsche Kinderhilfswerk, die Freiwillige Selbstkontrolle Multimedia-Diensteanbieter (FSM), die Freiwillige Selbstkontrolle Fernsehen (FSF) und Google Deutschland haben die Kinder-Video-Plattform juki.de gemeinsam entwickelt und am 7. Mai 2013 im Museum für Kommunikation in Berlin vorgestellt. Juki ist eine Plattform für Kinder, auf der sie Videos anschauen, kommentieren und bewerten können. Dabei können sie Videos aus unterschiedlichen Rubriken auswählen, zum Beispiel aus dem Bereich des Sports, aus der Tierwelt, der Schule oder aus dem Bereich allgemeinen Wissens. In der Rubrik Schule be

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Yadav, Madan Lal, and Basav Roychoudhury. "Effectiveness of Domain-Based Lexicons vis-à-vis General Lexicon for Aspect-Level Sentiment Analysis: A Comparative Analysis." Journal of Information & Knowledge Management 18, no. 03 (2019): 1950033. http://dx.doi.org/10.1142/s0219649219500333.

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One can either use machine learning techniques or lexicons to undertake sentiment analysis. Machine learning techniques include text classification algorithms like SVM, naive Bayes, decision tree or logistic regression, whereas lexicon-based sentiment analysis uses either general or domain-based lexicons. In this paper, we investigate the effectiveness of domain lexicons vis-à-vis general lexicon, wherein we have performed aspect-level sentiment analysis on data from three different domains, viz. car, guitar and book. While it is intuitive that domain lexicons will always perform better than g

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Du, Maokang, Xiaoguang Li, and Longyan Luo. "A Training-Optimization-Based Method for Constructing Domain-Specific Sentiment Lexicon." Complexity 2021 (February 5, 2021): 1–11. http://dx.doi.org/10.1155/2021/6152494.

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Sentiment analysis has been widely used in text mining of social media to discover valuable information from user reviews. Sentiment lexicon is an essential tool for sentiment analysis. Recent research studies indicate that constructing sentiment lexicons for special domains can achieve better results in sentiment analysis. However, it is not easy to construct a sentiment lexicon for a specific domain because most current methods highly depend on general sentiment lexicons and complex linguistic rules. In this paper, the construction of sentiment lexicon is transformed into a training-optimiza

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SGARBAS, KYRIAKOS N., NIKOS D. FAKOTAKIS, and GEORGE K. KOKKINAKIS. "TWO ALGORITHMS FOR INCREMENTAL CONSTRUCTION OF DIRECTED ACYCLIC WORD GRAPHS." International Journal on Artificial Intelligence Tools 04, no. 03 (1995): 369–81. http://dx.doi.org/10.1142/s0218213095000188.

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In this paper we present two algorithms for building lexicons in Directed Acyclic Word-Graphs (DAWGs). The two algorithms, one for deterministic and the other for non-deterministic DAWGs, can be used instead of the traditional subset construction method. Although the proposed algorithms do not produce the optimal DAWG (i.e., the one with the minimum number of states), they are simple, fast and able to build the DAWG incrementally, as new words are added to the lexicon. Thus, building large lexicons in a DAWG structure becomes an easy task, even for a modest computer.

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Sazzed, Salim. "BengSentiLex and BengSwearLex: creating lexicons for sentiment analysis and profanity detection in low-resource Bengali language." PeerJ Computer Science 7 (November 16, 2021): e681. http://dx.doi.org/10.7717/peerj-cs.681.

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Bengali is a low-resource language that lacks tools and resources for various natural language processing (NLP) tasks, such as sentiment analysis or profanity identification. In Bengali, only the translated versions of English sentiment lexicons are available. Moreover, no dictionary exists for detecting profanity in Bengali social media text. This study introduces a Bengali sentiment lexicon, BengSentiLex, and a Bengali swear lexicon, BengSwearLex. For creating BengSentiLex, a cross-lingual methodology is proposed that utilizes a machine translation system, a review corpus, two English sentim

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Zhao, Futao, Zhong Yao, Jing Luan, and Hao Liu. "Inducing stock market lexicons from disparate Chinese texts." Industrial Management & Data Systems 120, no. 3 (2019): 508–25. http://dx.doi.org/10.1108/imds-04-2019-0254.

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Purpose The purpose of this paper is to propose a methodology to construct a stock market sentiment lexicon by incorporating domain-specific knowledge extracted from diverse Chinese media outlets. Design/methodology/approach This paper presents a novel method to automatically generate financial lexicons using a unique data set that comprises news articles, analyst reports and social media. Specifically, a novel method based on keyword extraction is used to build a high-quality seed lexicon and an ensemble mechanism is developed to integrate the knowledge derived from distinct language sources.

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Pozharytska, Olena, Iryna Morozova, Kateryna Miliutina, Ganna Gusieva, and Olena Lenska. "The Computer Game as an Alternative Artistic Discourse." Theory and Practice in Language Studies 13, no. 2 (2023): 311–17. http://dx.doi.org/10.17507/tpls.1302.05.

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The article aimed to determine the specificity of computer game discourse, its features, key linguistic characteristics, and communicative features. The methodology included the analysis of computer game discourse materials, in particular, dictionary articles, texts of electronic messages, and computer conferences, as well as recordings of fragments of the spoken language of users and users of computer games. The specific feature of computer discourse is the selective combination of features, typical for other types and forms of communication. Computer discourse has some communicative features

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Mohamed, Ensaf Hussein, Mohammed ElSaid Moussa, and Mohamed Hassan Haggag. "An Enhanced Sentiment Analysis Framework Based on Pre-Trained Word Embedding." International Journal of Computational Intelligence and Applications 19, no. 04 (2020): 2050031. http://dx.doi.org/10.1142/s1469026820500315.

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Sentiment analysis (SA) is a technique that lets people in different fields such as business, economy, research, government, and politics to know about people’s opinions, which greatly affects the process of decision-making. SA techniques are classified into: lexicon-based techniques, machine learning techniques, and a hybrid between both approaches. Each approach has its limitations and drawbacks, the machine learning approach depends on manual feature extraction, lexicon-based approach relies on sentiment lexicons that are usually unscalable, unreliable, and manually annotated by human exper

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Gao, Dehong, Furu Wei, Wenjie Li, Xiaohua Liu, and Ming Zhou. "Cross-lingual Sentiment Lexicon Learning With Bilingual Word Graph Label Propagation." Computational Linguistics 41, no. 1 (2015): 21–40. http://dx.doi.org/10.1162/coli_a_00207.

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In this article we address the task of cross-lingual sentiment lexicon learning, which aims to automatically generate sentiment lexicons for the target languages with available English sentiment lexicons. We formalize the task as a learning problem on a bilingual word graph, in which the intra-language relations among the words in the same language and the inter-language relations among the words between different languages are properly represented. With the words in the English sentiment lexicon as seeds, we propose a bilingual word graph label propagation approach to induce sentiment polarit

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Wijayanti, Rini, and Andria Arisal. "Automatic Indonesian Sentiment Lexicon Curation with Sentiment Valence Tuning for Social Media Sentiment Analysis." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 1 (2021): 1–16. http://dx.doi.org/10.1145/3425632.

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A novel Indonesian sentiment lexicon (SentIL -- Sentiment Indonesian Lexicon) is created with an automatic pipeline; from creating sentiment seed words, adding new words with slang words, emoticons, and from the given dictionary and sentiment corpus, until tuning sentiment value with tagged sentiment corpus. It begins by taking seed words from WordNet Bahasa that mapped with sentiment value from English SentiWordNet . The seed words are enriched by combining the dictionary-based method with words’ synonyms and antonyms, and corpus-based methods with word embedding for word similarity that trai

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Serrano-Guerrero, Jesus, Bashar Alshouha, Francisco P. Romero, and Jose A. Olivas. "Affective Knowledge-enhanced Emotion Detection in Arabic Language: A Comparative Study." JUCS - Journal of Universal Computer Science 28, no. 7 (2022): 733–57. http://dx.doi.org/10.3897/jucs.72590.

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Online opinions/reviews contain a lot of sentiments and emotions that can be very useful, especially, for Internet suppliers which can know whether their services/products are meeting their customers&rsquo; expectations or not. To detect these sentiments and emotions, most applications resort to lexicon-based approaches. The major issue here is that most well-known emotion lexicons have been developed for English language; nevertheless, in other languages such as Arabic, there are fewer available tools, and many times, the quality of them is poor. The goal of this study is to compare the p

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Molina Beltrán, Carlos, Alejandra Andrea Segura Navarrete, Christian Vidal-Castro, Clemente Rubio-Manzano, and Claudia Martínez-Araneda. "Improving the affective analysis in texts." Electronic Library 37, no. 6 (2019): 984–1006. http://dx.doi.org/10.1108/el-11-2018-0219.

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Purpose This paper aims to propose a method for automatically labelling an affective lexicon with intensity values by using the WordNet Similarity (WS) software package with the purpose of improving the results of an affective analysis process, which is relevant to interpreting the textual information that is available in social networks. The hypothesis states that it is possible to improve affective analysis by using a lexicon that is enriched with the intensity values obtained from similarity metrics. Encouraging results were obtained when an affective analysis based on a labelled lexicon wa

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Gu, Wentao, Linghong Zhang, Houjiao Xi, and Suhao Zheng. "Stock Prediction Based on News Text Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 25, no. 5 (2021): 581–91. http://dx.doi.org/10.20965/jaciii.2021.p0581.

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With the vigorous development of information technology, the textual data of financial news have grown massively, and this ever-rich online news information can influence investors’ decision-making behavior, which affects the stock market. Thus, online news is an important factor affecting market volatility. Quantifying the sentiment of news media and applying it to stock-market prediction has become a popular research topic. In this study, a financial news sentiment lexicon and an auxiliary lexicon applicable to the financial field are constructed, and a sentiment index (SI) is constructed by

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Satauri, Imane, Boutaina Satouri, and Omar El Beqqali. "Big Data Analysis in Commercial Social Networks: Analysis of Twitter Reviews for Marketing Decision Making." European Journal of Information Technologies and Computer Science 3, no. 2 (2023): 1–6. http://dx.doi.org/10.24018/compute.2023.3.2.94.

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Content generated by users on commercial social networks about products and brands generates large volumes of data that can be transformed into relevant and useful recommendations for marketing decisions. Every day, consumers post their opinions online on social networks about products they have purchased and used, and companies are increasingly interested in tracking this information in real time for better decision making. The main problem is to extract key information from consumers' textual comments and use it automatically to measure the quality of products or brands. In this work, we pro

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van der Linden, Elisabeth, and Koenraad De Smedt. "Computerlexica Voor een Auteursysteem." Toegepaste Taalwetenschap in Artikelen 27 (January 1, 1987): 33–41. http://dx.doi.org/10.1075/ttwia.27.05lin.

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The accessibility of lexical information stored on computers is not only important for the human computer user, but also for programs that process natural language. The requirements with respect to the content and structure of a computer dictionary are different than for a printed dictionary and depend on the specific function of the language processing system. For example, the lexical knowledge needed for hyphenation is different from that for the correction of spelling errors. A number of such language manipulating modules which are designed in the context of the Nijmegen Language Technology

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Barbosa-Santillán, Liliana Ibeth, and Inmaculada Álvarez-de-Mon y-Rego. "Towards a Unified Sentiment Lexicon Based on Graphics Processing Units." Mathematical Problems in Engineering 2014 (2014): 1–19. http://dx.doi.org/10.1155/2014/429629.

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This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral{P,N,Z}depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach

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Feng, Jun, Cheng Gong, Xiaodong Li, and Raymond Y. K. Lau. "Automatic Approach of Sentiment Lexicon Generation for Mobile Shopping Reviews." Wireless Communications and Mobile Computing 2018 (August 12, 2018): 1–13. http://dx.doi.org/10.1155/2018/9839432.

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The dramatic increase in the use of smartphones has allowed people to comment on various products at any time. The analysis of the sentiment of users’ product reviews largely depends on the quality of sentiment lexicons. Thus, the generation of high-quality sentiment lexicons is a critical topic. In this paper, we propose an automatic approach for constructing a domain-specific sentiment lexicon by considering the relationship between sentiment words and product features in mobile shopping reviews. The approach first selects sentiment words and product features from original reviews and mines

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Moreno, Fábio Carlos, Cinthyan Sachs C. de Barbosa, and Edio Roberto Manfio. "Hash Tables for a Digital Lexicon." Revista de Informática Teórica e Aplicada 28, no. 2 (2021): 25–38. http://dx.doi.org/10.22456/2175-2745.107128.

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This paper deals with the construction of digital lexicons within the scope of Natural Language Processing. Data Structures called Hash Tables have demonstrated to generate good results for Natural Language Interface for Databases and have data dispersion, response speed and programming simplicity as main features. The storage of the desired information is done by associating a key through the hashing functions that is responsible for distributing the information in this table. The objective of this paper is to present the tool called Visual TaHs that uses a sparse table to a real lexicon (Lex

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Shmelyov, Alexander G., and Vladimir I. Pokhil'ko. "A taxonomy‐oriented study of Russian personality‐trait names." European Journal of Personality 7, no. 1 (1993): 1–17. http://dx.doi.org/10.1002/per.2410070102.

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This is the first English report of the procedures and findings from a project whose aim is the development of a computerized thesaurus of Russian personality‐descriptive terms as a special instrument for providing computer‐based test interpretations. Our empirical analyses were carried out in two waves: in 1983–1984, we examined the relations among 1530 trait–descriptive terms; and in 1986–1987, we examined the rela tions among 1650 such terms. Our present Russian personality lexicon includes 2090 trait terms, of which 68 per cent are adjectives and 32 per cent are nouns. Here, we present som

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Lepe-Faúndez, Manuel, Alejandra Segura-Navarrete, Christian Vidal-Castro, Claudia Martínez-Araneda, and Clemente Rubio-Manzano. "Detecting Aggressiveness in Tweets: A Hybrid Model for Detecting Cyberbullying in the Spanish Language." Applied Sciences 11, no. 22 (2021): 10706. http://dx.doi.org/10.3390/app112210706.

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In recent years, the use of social networks has increased exponentially, which has led to a significant increase in cyberbullying. Currently, in the field of Computer Science, research has been made on how to detect aggressiveness in texts, which is a prelude to detecting cyberbullying. In this field, the main work has been done for English language texts, mainly using Machine Learning (ML) approaches, Lexicon approaches to a lesser extent, and very few works using hybrid approaches. In these, Lexicons and Machine Learning algorithms are used, such as counting the number of bad words in a sent

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Jurado, Francisco, and Pilar Rodriguez. "An experience in automatically building lexicons for affective computing in multiple target languages." Computer Science and Information Systems 16, no. 1 (2019): 273–87. http://dx.doi.org/10.2298/csis171001036j.

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Affective Computing in text attempts to identify the emotional charge reflected in it, trying to analyse the moods transmitted while writing. There are several techniques and approaches to perform Affective Computing in texts, but lexicons are their common point. However, it is difficult to find solutions for specific languages different from English. Thus, this article presents an experience in automatically generating lexicons to perform Affective Computing following a multiple- target languages approach. The experience starts with some initial seeds of words in English that define the emoti

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Bagherzadeh, Sayeh, Sajjad Shokouhyar, Hamed Jahani, and Marianna Sigala. "A generalizable sentiment analysis method for creating a hotel dictionary: using big data on TripAdvisor hotel reviews." Journal of Hospitality and Tourism Technology 12, no. 2 (2021): 210–38. http://dx.doi.org/10.1108/jhtt-02-2020-0034.

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Purpose Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons. Design/methodology/approach Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel ba

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Kumari, Lalita, Sukhdeep Singh, Vaibhav Varish Singh Rathore, and Anuj Sharma. "Lexicon and attention based handwritten text recognition system." Machine Graphics and Vision 31, no. 1/4 (2022): 75–92. http://dx.doi.org/10.22630/mgv.2022.31.1.4.

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The handwritten text recognition problem is widely studied by the researchers of computer vision community due to its scope of improvement and applicability to daily lives. It is a sub-domain of pattern recognition. Due to advancement of computational power of computers since last few decades neural networks based systems heavily contributed towards providing the state-of-the-art handwritten text recognizers. In the same direction, we have taken two state-of-the art neural networks systems and merged the attention mechanism with it. The attention technique has been widely used in the domain of

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Boguraev, Branimir K. "Building a Lexicon: The Contribution of Computers." International Journal of Lexicography 4, no. 3 (1991): 227–60. http://dx.doi.org/10.1093/ijl/4.3.227.

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Lynnyk, Yurii, та Iryna Navrotska. "THE OUTLINE OF CONTEMPORARY ENGLISH AND UKRAINIAN СOMPUTER SLANG LEXICON". RESEARCH TRENDS IN MODERN LINGUISTICS AND LITERATURE 5 (27 грудня 2022): 4–15. http://dx.doi.org/10.29038/2617-6696.2022.5.4.15.

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This article addresses computer English and Ukrainian computer slang as a lexical subsystem evoked to provide the Internet community with effective communication means. Slang lexicon fulfills a number of functions depending on the contextual features, level of professionalism of language users and their interest. Although it is argued by some scholars that slang words rarely undergo semantic changes, it has been revealed that in the process of adaptation in the target language lexical and grammatical transformations typically occur in computer slang. In particular, English computer slang words

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Bablu Singh, Y., Th Mamata Devi, and Ch Yashawanta Singh. "Manipuri Morphological Analysis." Asian Journal of Computer Science and Technology 9, no. 2 (2020): 4–10. http://dx.doi.org/10.51983/ajcst-2020.9.2.2174.

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Morphological analysis is the basic foundation in Natural Language Processing applications including Syntax Parsing, Machine Translation (MT), Information Retrieval (IR) and Automatic Indexing. Morphological Analysis can provide valuable information for computer based linguistics task such as Lemmatization and studies of internal structure of the words or the feature values of the word. Computational Morphology is the application of morphological rules in the field of Computational Linguistics, and it is the emerging area in AI, which studies the structure of words, which are formed by combini

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Liu, Yongquan. "New development of Chinese lexicon." Journal of Asian Pacific Communication 11, no. 2 (2001): 255–62. http://dx.doi.org/10.1075/japc.11.2.08liu.

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By way of relevant examples, this article introduces the problem of Chinese language modernization and the absorption of foreign and new technological words into Modern Chinese. It is suggested that the use of Pinyn in the People’s Republic of China has opened the door to the use of Roman letters in weird words which combine Chinese characters with Roman letters. As China moves further into the computer age, the paper speculates on the superior efficiency of phonetics scripts and the dilemma they will present to Chinese authorities in choosing between tradition and modern efficiency.

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Lee, Jeongmin, Sanghee Kim, Bong Joo Kang, Sung Hun Kim, and Ga Eun Park. "Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions." Medical Ultrasonography 21, no. 3 (2019): 239. http://dx.doi.org/10.11152/mu-1889.

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Aim: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions.Materials and methods: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the ne

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Madhvanath, S., E. Kleinberg, and V. Govindaraju. "Empirical Design of a Multi-Classifier Thresholding/Control Strategy for Recognition of Handwritten Street Names." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 06 (1997): 933–46. http://dx.doi.org/10.1142/s0218001497000421.

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A central task in the interpretation of handwritten US postal addresses is the off-line recognition of the street name. A lexicon of candidate street names may be extracted from a database of postal delivery points (DPF) by first locating and recognizing numeric fields such as the ZIP code and strewet number. The off-line handwritten word recognition (HWR) task is made difficult by the unconstrained, omni-scriptor nature of the input, and incomplete lexicons resulting from errors in processing numeric fields and intrinsic deficiencies in the DPF. In this paper, we describe an empirical approac

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Alharbi, Abdulrahman, Rafaa Aljurbua, Shelly Gupta, and Zoran Obradovic. "TriLex: A fusion approach for unsupervised sentiment analysis of short texts." PLOS ONE 20, no. 4 (2025): e0317100. https://doi.org/10.1371/journal.pone.0317100.

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In recent years, online customer reviews and social media platforms have significantly impacted individuals’ daily lives. Despite the generally short nature of textual content on these platforms, they convey a wide range of user sentiments. However, sentiment analysis of short texts poses a challenge due to their context limitations. In addition, traditional supervised machine learning methods often struggle with the dynamic nature of sentiment expression and the scarcity of labeled data, which is a cost-efficiency issue. To address these challenges, this paper proposes TriLex, a novel unsuper

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MARTIN, Willy. "On the Dynamic Organization of (Computer) Lexicons." Cahiers de l'Institut de Linguistique de Louvain 17, no. 1 (1991): 43–50. http://dx.doi.org/10.2143/cill.17.1.2016695.

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Chakraverty, Shampa, Srishti Sharma, and Ikshu Bhalla. "Emotion–Location Mapping and Analysis Using Twitter." Journal of Information & Knowledge Management 14, no. 03 (2015): 1550022. http://dx.doi.org/10.1142/s0219649215500227.

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The ever-increasing amount of text generated by Twitter users contains a wealth of information about the users' state of mind. Over the years, researchers have tapped upon this resource and proposed a number of lexicons and techniques for analysing the polarity of sentiments expressed by tweets. However, we need to delve deeper to extract the emotions conveyed by them — a research direction that had not received adequate attention so far. Through this work we develop a novel Emotion Analysis lexicon that was compiled by integrating information from the domain of psychology, the lexical ontolog

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Turunen, Risto, Ilari Taskinen, Lauri Uusitalo, and Ville Kivimäki. "Mining Emotions from the Finnish War Letter Collection, 1939– 1944." Digital Humanities in the Nordic and Baltic Countries Publications 4, no. 1 (2022): 135–44. http://dx.doi.org/10.5617/dhnbpub.11281.

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Our paper analyses emotional language used by Finnish soldiers and civilians in their private communication during World War II. The dataset consists of 7,000 handwritten letters converted into a machine-readable corpus with rich metadata. The dataset offers a unique opportunity to make a statistical analysis of people’s emotional responses to the war. We engage in key questions of the cultural history of war, such as the connection between soldiers’ emotional language and violence: did soldiers’ emotional language become more laconic in the course of the war? While computational approaches to

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Dmitrieva, Elena V. "Age Conditionality of Computer Terminology in Individual Lexicon (Experimental Research)." Humanitarian and pedagogical Research 5, no. 4 (2021): 16–21. http://dx.doi.org/10.18503/2658-3186-2021-5-4-16-21.

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Karpov, Alexey Anatolyevich, and Ildar Amirovich Kagirov. "Lexicon Formalization for a Computer System of Sign Language Synthesis." SPIIRAS Proceedings 1, no. 16 (2014): 123. http://dx.doi.org/10.15622/sp.16.4.

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Groot, Peter. "Computer Ondersteunde Vreemde-Taalverwerving op de Hogere Niveaus." Toegepaste Taalwetenschap in Artikelen 61 (January 1, 1999): 111–26. http://dx.doi.org/10.1075/ttwia.61.10gro.

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The interactive self-study programme (called CAVOCA: Computer Assisted VOCabulary Acquisition) is based on generally accepted theories about the mental lexicon (cf. Aitchison's Words in the Mind) and the "levels of processing" theory first advanced by Craik and Lockhart. The programme constitutes a systematic attempt to operationalize the theoretical analysis of the word learning process that recognizes various stages in this same process: learning about the various features of the word, embedding it in various networks, consolidation etc. leading to efficient lexical retrieval. With this end

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Souici-Meslati, Labiba, and Mokhtar Sellami. "A Hybrid Neuro-Symbolic Approach for Arabic Handwritten Word Recognition." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 1 (2006): 17–25. http://dx.doi.org/10.20965/jaciii.2006.p0017.

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In this article, we suggest a system that automatically constructs knowledge based artificial neural networks (KBANN) for the holistic recognition of handwritten Arabic words in limited lexicons. To build a neuro-symbolic KBANN classifier for a given vocabulary, ideal samples of its words are first submitted to a structural feature extraction module. The analysis of the presence and possible occurrence numbers for these features in the considered lexicon enables to generate a symbolic knowledge base reflecting a hierarchical classification of the words. A rules-to-network translation algorithm

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Boukabous, Mohammed, and Mostafa Azizi. "Crime prediction using a hybrid sentiment analysis approach based on the bidirectional encoder representations from transformers." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 1131. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp1131-1139.

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Sentiment analysis (SA) is widely used today in many areas such as crime detection (security intelligence) to detect potential security threats in realtime using social media platforms such as Twitter. The most promising techniques in sentiment analysis are those of deep learning (DL), particularly bidirectional encoder representations from transformers (BERT) in the field of natural language processing (NLP). However, employing the BERT algorithm to detect crimes requires a crime dataset labeled by the lexiconbased approach. In this paper, we used a hybrid approach that combines both lexicon-

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Yu, Lei, Yu Wu, Jie Yang, and Yunkai Zhang. "Bullet Subtitle Sentiment Classification Based on Affective Computing and Ensemble Learning." Wireless Communications and Mobile Computing 2021 (June 24, 2021): 1–9. http://dx.doi.org/10.1155/2021/5563104.

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The bullet subtitle reflects a kind of instant feedback from the user to the current video. It is generally short but contains rich sentiment. However, the bullet subtitle has its own unique characteristics, and the effect of applying existing sentiment classification methods to the bullet subtitle sentiment classification problem is not ideal. First, since bullet subtitles usually contain a large number of buzzwords, existing sentiment lexicons are not applicable, we propose Chinese Bullet Subtitle Sentiment Lexicon on the basis of existing sentiment lexicons. Second, considering that some tr

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Cmejrek, Martin, and Jan Curín. "Automatic Extraction of Terminological Translation Lexicon from Czech-English Parallel Texts." Text Corpora and Multilingual Lexicography 6, no. 3 (2001): 1–12. http://dx.doi.org/10.1075/ijcl.6.si.02cme.

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We present experimental results of an automatic extraction of a Czech-English translation dictionary. Two different bilingual corpora (119,886 sentence pairs computer-oriented and 58,137 journalistic corpora) were created. We used the length-based statistical method for sentence alignment (Gale and Church 1991) and noun phrase marker working with regular grammar and probabilistic model (Brown et al. 1993) for dictionary extraction. Resulting dictionaries’ size varies around 6,000 entries. After significance filtering, weighted precision is 86.4% for computer-oriented and 70.7% for journalistic

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Kim, Jonghoon, Martin Fischer, John Kunz, and Raymond Levitt. "Semiautomated Scaffolding Planning: Development of the Feature Lexicon for Computer Application." Journal of Computing in Civil Engineering 29, no. 5 (2015): 04014079. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000399.

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Palapanidi, Kiriakí, and María Pilar Agustín Llach. "Visualizando el lexicón mental." RILEX. Revista sobre investigaciones léxicas 8, no. 2 (2025): 171–202. https://doi.org/10.17561/rilex.8.2.9504.

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In recent years, numerous digital tools specialized in linguistic research have been developed. This study presents three computer tools —Dispografo, Gephi, and Lexpro— used for the psycholinguistic analysis of the available lexicon. These tools represent lexical associations in the mental lexicon through graph theory, accessed via a semantic fluency test: the lexical availability test. The study explores the concepts of lexical availability, semantic networks, and lexical graphs, which are fundamental to these programs for analyzing and visualizing lexical relationships. Additionally, the fun

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Nasution, Arbi Haza, Yohei Murakami, and Toru Ishida. "Plan Optimization to Bilingual Dictionary Induction for Low-resource Language Families." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 2 (2021): 1–28. http://dx.doi.org/10.1145/3448215.

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Creating bilingual dictionary is the first crucial step in enriching low-resource languages. Especially for the closely related ones, it has been shown that the constraint-based approach is useful for inducing bilingual lexicons from two bilingual dictionaries via the pivot language. However, if there are no available machine-readable dictionaries as input, we need to consider manual creation by bilingual native speakers. To reach a goal of comprehensively create multiple bilingual dictionaries, even if we already have several existing machine-readable bilingual dictionaries, it is still diffi

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Tun, Yin Min, and Myo Khaing. "A large-scale sentiment analysis using political tweets." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 6913. http://dx.doi.org/10.11591/ijece.v13i6.pp6913-6925.

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<p><span lang="EN-US">Twitter has become a key element of political discourse in candidates’ campaigns. The political polarization on Twitter is vital to politicians as it is a popular public medium to analyze and predict public opinion concerning political events. The analysis of the sentiment of political tweet contents mainly depends on the quality of sentiment lexicons. Therefore, it is crucial to create sentiment lexicons of the highest quality. In the proposed system, the domain-specific of the political lexicon is constructed by using the supervised approach to extract extre

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Sdobnova, A. P. "Current Computer Language in the Associations of schoolchildren." Izvestiya of Saratov University. Philology. Journalism 10, no. 3 (2010): 13–18. http://dx.doi.org/10.18500/1817-7115-2010-10-3-13-18.

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The work is based on the materials of the Associative Dictionary of Saratov schoolchildren; it represents the research of the structure of the so-called computer lexicon in the associations of schoolchildren. It is shown that the computer words are a part of the vocabulary of all age groups, and that their importance increases as the schoolchildren become more mature. The data of the associative dictionary allow to reveal a number of factors (age, sex, place of residence, type of educational institution) defining unequal importance of the computer vocabulary in the language consciousness of di

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Hanif, Kharis, Arif Fadllullah, Novita Ranti Muntiari, and Irgi Ahmad Fahrezi. "A Comparative Sentiment Analysis of Computer Engineering Student Feedback Using Decision Trees and SVM." Jurnal Inotera 10, no. 1 (2025): 71–82. https://doi.org/10.31572/inotera.vol10.iss1.2025.id436.

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The University of Borneo Tarakan, like many Indonesian universities, is committed to continuous quality improvement in education services. A crucial aspect of this improvement is gathering and analyzing student feedback to enhance lecturer performance. This research focuses on analyzing student comments using sentiment analysis, a technique that categorizes text into positive, negative, and neutral sentiments. To achieve this, two machine learning algorithms were employed: Decision Trees and Support Vector Machines (SVM). The research involved two approaches: Lexicon-Based Sentiment Analysis a

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Zachar, István. "The feasibility of segmentation of protolanguage." Interaction Studies 12, no. 1 (2011): 1–35. http://dx.doi.org/10.1075/is.12.1.01zac.

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An important question in language evolution is whether segmentation as a linguistic process is able to yield compositionality. Segmentation is hypothesized to be a process to bridge the gap between holistic and compositional lexicons. However, to date no thorough analytical method has been provided to test the feasibility of segmentation. In this paper, an analytical model is presented that can predict the probability of encountering various kinds of overlaps by observing utterance pairs, and the probability of finding confirmation in the language for newly extracted segments. Language users s

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