Journal articles: 'Text-based'

1

SI, Qin, Li ZHANG, and De-liang LIAN. "Text watermarking based on text feature." Journal of Computer Applications 29, no. 9 (2009): 2348–50. http://dx.doi.org/10.3724/sp.j.1087.2009.02348.

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Farah, Qasim Ahmed Alyousuf, and Din Roshidi. "Analysis review on feature-based and word-rule based techniques in text steganography." Bulletin of Electrical Engineering and Informatics 9, no. 2 (2020): 764–70. https://doi.org/10.11591/eei.v9i2.2069.

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This paper presents several techniques used in text steganography in term of feature-based and word-rule based. Additionally, it analyses the performance and the metric evaluation of the techniques used in text steganography. This paper aims to identify the main techniques of text steganography, which are feature-based, and word-rule based, to recognize the various techniques used with them. As a result, the primary technique used in the text steganography was feature-based technique due to its simplicity and secured. Meanwhile, the common parameter metrics utilized in text steganography were security, capacity, robustness, and embedding time. Future efforts are suggested to focus on the methods used in text steganography.

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D., Mhamdi. "Job Recommendation System based on Text Analysis." Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (2020): 1025–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201575.

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Hussain Zargar, Khalid, and Manzoor Ahmad Chachoo. "Content Based Text Classification Using Morkov Models." International Journal of Scientific Engineering and Research 3, no. 6 (2015): 48–52. https://doi.org/10.70729/ijser15239.

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Lockwood, Robert, and Kevin Curran. "Text based steganography." International Journal of Information Privacy, Security and Integrity 3, no. 2 (2017): 134. http://dx.doi.org/10.1504/ijipsi.2017.088700.

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Lockwood, Robert, and Kevin Curran. "Text based steganography." International Journal of Information Privacy, Security and Integrity 3, no. 2 (2017): 134. http://dx.doi.org/10.1504/ijipsi.2017.10009581.

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YOSHIMI, TAKEHIKO, JIRI JELINEK, OSAMU NISHIDA, NAOYUKI TAMURA, and HARUO MURAKAMI. "Text Analysis based on Text-Wide Grammar." Journal of Natural Language Processing 4, no. 1 (1997): 3–21. http://dx.doi.org/10.5715/jnlp.4.3.

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Li, De, Xue Zhe Jin, and LiHua Cui. "Text recognition algorithm based on text features." International Journal of Multimedia and Ubiquitous Engineering 11, no. 5 (2016): 209–20. http://dx.doi.org/10.14257/ijmue.2016.11.5.19.

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Chatterjee, Ayan, Gourab Dolui, and Dr Uttam Kumar Roy. "Text Based Steganography – A Theoritical Proposal of Text Based Hiding Strategy." International Journal of Scientific and Engineering Research 6, no. 11 (2015): 625–29. http://dx.doi.org/10.14299/ijser.2015.11.005.

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Chatterjee, Ayan, Gourab Dolui, and Dr Uttam Kumar Roy. "Text Based Steganography - A Theoritical Proposal of Text Based Hiding Strategy." International Journal of Scientific and Engineering Research 6, no. 11 (2015): 625–29. http://dx.doi.org/10.14299/ijser.2015.11.010.

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S Gaikwad, Vijayendra. "Graph Based Abstractive Text Summarization of YouTube Comments." International Journal of Science and Research (IJSR) 12, no. 6 (2023): 913–18. http://dx.doi.org/10.21275/sr23604110517.

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Kanakanapuri, Nishitha, Likhitha Meher Medisetty, Sudeeshna Bhutham, and Premkumar Chithaluru. "Interactive OCR Editor for Image-Based Text Editing." International Journal of Research Publication and Reviews 6, no. 5 (2025): 14779–91. https://doi.org/10.55248/gengpi.6.0525.1959.

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S. J, Rexline, Robert L, and Trujilla Lobo.F. "Dictionary Based Text Filter for Lossless Text Compression." International Journal of Computer Trends and Technology 49, no. 3 (2017): 143–49. http://dx.doi.org/10.14445/22312803/ijctt-v49p122.

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Elazar, Yanai, Victoria Basmov*, Yoav Goldberg, and Reut Tsarfaty. "Text-based NP Enrichment." Transactions of the Association for Computational Linguistics 10 (2022): 764–84. http://dx.doi.org/10.1162/tacl_a_00488.

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Abstract Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding. However, only a fraction of such relations is covered by standard NLP tasks and benchmarks nowadays. In this work, we propose a novel task termed text-based NP enrichment (TNE), in which we aim to enrich each NP in a text with all the preposition-mediated relations—either explicit or implicit—that hold between it and other NPs in the text. The relations are represented as triplets, each denoted by two NPs related via a preposition. Humans recover such relations seamlessly, while current state-of-the-art models struggle with them due to the implicit nature of the problem. We build the first large-scale dataset for the problem, provide the formal framing and scope of annotation, analyze the data, and report the results of fine-tuned language models on the task, demonstrating the challenge it poses to current technology. A webpage with a data-exploration UI, a demo, and links to the code, models, and leaderboard, to foster further research into this challenging problem can be found at: yanaiela.github.io/TNE/.

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Ferhat, Ensar. "Schema-based text comprehension." Educational Research and Reviews 10, no. 18 (2015): 2568–74. http://dx.doi.org/10.5897/err2015.2469.

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Hoberg, Gerard, and Gordon M. Phillips. "Text-Based Industry Momentum." Journal of Financial and Quantitative Analysis 53, no. 6 (2018): 2355–88. http://dx.doi.org/10.1017/s0022109018000479.

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We test the hypothesis that low-visibility shocks to text-based network industry peers can explain industry momentum. We consider industry peer firms identified through 10-K product text and focus on economic peer links that do not share common Standard Industrial Classification (SIC) codes. Shocks to less visible peers generate economically large momentum profits and are stronger than own-firm momentum variables. More visible traditional SIC-based peers generate only small, short-lived momentum profits. Our findings are consistent with momentum profits arising partially from inattention to economic links of less visible industry peers.

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LEWITTER, F. "Text-based database searching." Trends in Biotechnology 16 (November 1998): 3–5. http://dx.doi.org/10.1016/s0167-7799(98)00126-7.

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Norvig, Peter. "Text-based intelligent systems." Artificial Intelligence 66, no. 1 (1994): 181–88. http://dx.doi.org/10.1016/0004-3702(94)90007-8.

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Moffat, Alistair. "Word-based text compression." Software: Practice and Experience 19, no. 2 (1989): 185–98. http://dx.doi.org/10.1002/spe.4380190207.

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Zhumagulova, Erkeaiym Zhamyrkanovna. "TEXT - BASED TEACHING PRACTICES." Вестник Международного Университета Кыргызстана, no. 3 (2022): 107–10. http://dx.doi.org/10.53473/16946324_2022_3_107.

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Schwarz, Christoph. "Content based text handling." Information Processing & Management 26, no. 2 (1990): 219–26. http://dx.doi.org/10.1016/0306-4573(90)90027-y.

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Shrabanti, Mandal, and Kumar Singh Girish. "LSA Based Text Summarization." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 2 (2020): 150–56. https://doi.org/10.35940/ijrte.B3288.079220.

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In this study we propose an automatic single document text summarization technique using Latent Semantic Analysis (LSA) and diversity constraint in combination. The proposed technique uses the query based sentence ranking. Here we are not considering the concept of IR (Information Retrieval) so we generate the query by using the TF-IDF(Term Frequency-Inverse Document Frequency). For producing the query vector, we identify the terms having the high IDF. We know that LSA utilizes the vectorial semantics to analyze the relationships between documents in a corpus or between sentences within a document and key terms they carry by producing a list of ideas interconnected to the documents and terms. LSA helps to represent the latent structure of documents. For selecting the sentences from the document Latent Semantic Indexing (LSI) is used. LSI helps to arrange the sentences with its score. Traditionally the highest score sentences have been chosen for summary but here we calculate the diversity between chosen sentences and produce the final summary as a good summary should have maximum level of diversity. The proposed technique is evaluated on OpinosisDataset1.0.

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Qi, Wenfa, Wei Guo, Tong Zhang, Yuxin Liu, Zongming Guo, and Xifeng Fang. "Robust authentication for paper-based text documents based on text watermarking technology." Mathematical Biosciences and Engineering 16, no. 4 (2019): 2233–49. http://dx.doi.org/10.3934/mbe.2019110.

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Satyabrata, Aich, Chakraborty Sabyasachi, and Kim Hee-Cheol. "Convolutional neural network-based model for web-based text classification." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5185–91. https://doi.org/10.11591/ijece.v9i6.pp5185-5191.

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There is an increasing amount of text data available on the web with multiple topical granularities; this necessitates proper categorization/classification of text to facilitate obtaining useful information as per the needs of users. Some traditional approaches such as bag-of-words and bag-of-ngrams models provide good results for text classification. However, texts available on the web in the current state contain high event-related granularity on different topics at different levels, which may adversely affect the accuracy of traditional approaches. With the invention of deep learning models, which already have the capability of providing good accuracy in the field of image processing and speech recognition, the problems inherent in the traditional text classification model can be overcome. Currently, there are several deep learning models such as a convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short term memory that are widely used for various text-related tasks; however, among them, the CNN model is popular because it is simple to use and has high accuracy for text classification. In this study, classification of random texts on the web into categories is attempted using a CNN-based model by changing the hyperparameters and sequence of text vectors. We attempt to tune every hyperparameter that is unique for the classification task along with the sequences of word vectors to obtain the desired accuracy; the accuracy is found to be in the range of 85–92%. This model can be considered as a reliable model and applied to solve real-world problem or extract useful information for various text mining applications.

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More, Prof Vijay, Ms Ankita Shetty, and Ms Aishwarya Mapara Mr Rahul Ghuge Mr Rohit Sharma. "Employee Data Mining Based on Text and Image Processing." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 379–81. http://dx.doi.org/10.31142/ijtsrd10791.

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Vijayabaskar, Dr S., Ms S. Angaleeswari, Ms S. Devibala, Ms R. Ramya Shree, and Ms M. Seethalakshmi. "Hand Gesture Based Voice and Text Output Using IoT." International Journal of Research Publication and Reviews 5, no. 11 (2024): 6259–62. https://doi.org/10.55248/gengpi.5.1124.3403.

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BASYSTIUK, Oleh, and Nataliia MELNYKOVA. "MULTIMODAL SPEECH RECOGNITION BASED ON AUDIO AND TEXT DATA." Herald of Khmelnytskyi National University. Technical sciences 313, no. 5 (2022): 22–25. http://dx.doi.org/10.31891/2307-5732-2022-313-5-22-25.

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Systems of machine translation of texts from one language to another simulate the work of a human translator. Their performance depends on the ability to understand the grammar rules of the language. In translation, the basic units are not individual words, but word combinations or phraseological units that express different concepts. Only by using them, more complex ideas can be expressed through the translated text. The main feature of machine translation is different length for input and output. The ability to work with different lengths of input and output provides us with the approach of recurrent neural networks. A recurrent neural network (RNN) is a class of artificial neural network that has connections between nodes. In this case, a connection refers to a connection from a more distant node to a less distant node. The presence of connections allows the RNN to remember and reproduce the entire sequence of reactions to one stimulus. From the point of view of programming, such networks are analogous to cyclic execution, and from the point of view of the system, such networks are equivalent to a state machine. RNNs are commonly used to process word sequences in natural language processing. Usually, a hidden Markov model (HMM) and an N-program language model are used to process a sequence of words. Deep learning has completely changed the approach to machine translation. Researchers in the deep learning field has created simple solutions based on machine learning that outperform the best expert systems. In this paper was reviewed the main features of machine translation based on recurrent neural networks. The advantages of systems based on RNN using the sequence-to-sequence model against statistical translation systems are also highlighted in the article. Two machine translation systems based on the sequence-to-sequence model were constructed using Keras and PyTorch machine learning libraries. Based on the obtained results, libraries analysis was done, and their performance comparison.

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Vidhya, P. M., and Varghese Paul. "Unicode-based method for text steganography with malayalam text." Journal of Intelligent & Fuzzy Systems 28, no. 4 (2015): 1591–600. http://dx.doi.org/10.3233/ifs-141444.

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Bichi, Abdulkadir Abubakar, Ruhaidah Samsudin, Rohayanti Hassan, Layla Rasheed Abdallah Hasan, and Abubakar Ado Rogo. "Graph-based extractive text summarization method for Hausa text." PLOS ONE 18, no. 5 (2023): e0285376. http://dx.doi.org/10.1371/journal.pone.0285376.

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Automatic text summarization is one of the most promising solutions to the ever-growing challenges of textual data as it produces a shorter version of the original document with fewer bytes, but the same information as the original document. Despite the advancements in automatic text summarization research, research involving the development of automatic text summarization methods for documents written in Hausa, a Chadic language widely spoken in West Africa by approximately 150,000,000 people as either their first or second language, is still in early stages of development. This study proposes a novel graph-based extractive single-document summarization method for Hausa text by modifying the existing PageRank algorithm using the normalized common bigrams count between adjacent sentences as the initial vertex score. The proposed method is evaluated using a primarily collected Hausa summarization evaluation dataset comprising of 113 Hausa news articles on ROUGE evaluation toolkits. The proposed approach outperformed the standard methods using the same datasets. It outperformed the TextRank method by 2.1%, LexRank by 12.3%, centroid-based method by 19.5%, and BM25 method by 17.4%.

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Phan Thi Hoai, Nguyen Minh Phuc, Nguyen Huu Hieu, Dr Thuy Pham Thanh, and Le Thi Lan. "Tabular text embedding for Vietnamese text-based person search." Journal of Military Science and Technology 93, no. 93 (2024): 128–36. http://dx.doi.org/10.54939/1859-1043.j.mst.93.2024.128-136.

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Vietnamese text-based person search is still a challenging problem with the limited dataset of Vietnamese descriptions. The current popular approach to this problem is Deep Neural Networks (DNNs), and recently, transformer networks have been more favored because of their outperformance over CNN and RNN networks for both vision and natural language processing tasks. However, DNN, or transformer networks, require a large amount of training data and computing time for efficient learning of visual and textual features. This brings a burden for implementing Vietnamese text-based person search by DNN, or transformer networks. Towards building a Vietnamese text-based person search system on a scarce resource dataset of Vietnamese descriptive sentences with low computing cost, in this work, we propose to apply the transformer-based architecture named TabTransformer for contextual embedding of the noun phrases chunked from the Vietnamese descriptive sentences. This is the first time the TabTransformer network has been deployed together with CNN and RNN architectures for Vietnamese text-based person search. The experimental results on a limited dataset of 3000VnPersonSearch show the better recognition accuracy of the proposed method compared to the baseline method by about 7.5% at Rank 1. In addition, the computing time of our method is more effective than the baseline method.

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Linus, Kiptanui, C. J. Prabhakar, and S. R. Shrinivasa. "Rectification of Curved Scene Text Based on B-Spline Curve Fitting." Indian Journal Of Science And Technology 17, no. 32 (2024): 3305–17. http://dx.doi.org/10.17485/ijst/v17i32.2402.

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Objectives: In this study, we proposed suitable technique for rectification of curved scene text which is followed by recognition of rectified text in order to improve the accuracy of the existing techniques. Methods: In order to rectify curved text, initially, we perform curved text detection using Look More Than Twice (LOMT) model which detects and locates curved text. The detected text area is binarized through adaptive binarizaton technique. Then, we rectify the detected curved text through B-spline based curve fitting which align the curved text into straight line. The rectified text is feed to our recognition module where, we segment the rectified text using Low Variation Extremal Regions (ER) technique and extract the Pyramid Histogram of Oriented Gradients (PHOG) features from the segmented texts. Finally, we perform recognition of text using Tesseract OCR. The rectification and recognition performance of the proposed method on CUTE80 dataset was evaluated using Mean Square Error (MSE) metric and word level recognition accuracy respectively. We compared the recognition results of the proposed method with state-of-the-art methods using challenging benchmarks such as ICDAR2013, Street View Text (SVT), IIIT5k-Words (IIITK), ICDAR2015, SVT-Perspective (SVTP) and CUTE80 dataset based on word level recognition accuracy. Findings: From the experimental results, it is observed that the proposed rectification method removes highest error (MSE is minimum) of 99.34% for perspective text with large angle. Also, the proposed recognition module achieves highest recognition accuracy of 93.80% compared to the state-of-the-art methods on the selected six benchmarks. Novelty: We proposed a technique for rectifying curved scene text using B-spline curve-fitting technique followed by recognition using handcrafted features. The novelty of the proposed method is that we employ B-spline curve fitting in order to compute local transformation of individual character followed by rectification of each individual character through the computation of normal vectors. The recognition of rectified text is done through the handcrafted features. Keywords: Rectification, Curved Scene Text, Text Detection, Curve Fitting, Recognition, BSpline

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Sukhaniuk, I. S., K. R. Potapova, M. V. Nalyvaichuk, and L. B. Vovk. "TEXT SUMMARIZATION BASED ON TOPICRANK METHOD AND TEXT-TO-TEXT TRANSFORMER NEURAL NETWORK." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences, no. 6 (2023): 147–54. http://dx.doi.org/10.32782/2663-5941/2023.6/22.

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Schraw, Gregory, Suzanne E. Wade, and Carol A. Kardash. "Interactive effects of text-based and task-based importance on learning from text." Journal of Educational Psychology 85, no. 4 (1993): 652–61. http://dx.doi.org/10.1037/0022-0663.85.4.652.

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R., Kohila* Dr. K. Arunesh. "TEXT MINING: TEXT SIMILARITY MEASURE FOR NEWS ARTICLES BASED ON STRING BASED APPROACH." Global Journal of Engineering Science and Research Management 3, no. 7 (2016): 35–42. https://doi.org/10.5281/zenodo.57373.

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Now-a-days, the documents similarity measuring plays an important role in text related researches. There are many applications in document similarity measures such as plagiarism detection, document clustering, automatic essay scoring, information retrieval and machine translation. String Based Similarity, Knowledge Based Similarity and Corpus Based Similarity are the three major approaches proposed by the most of the   researchers to solve the problems in document similarity. In this paper, the String Based Similarity measure Term Based algorithm Cosine Similarity is used to measuring the similarity between the documents. The nouns in the documents are extracted and context word synset are also extracted using WordNet. The bigram dataset is created based on Context words. In this proposed method the similarity measure between the documents is measured using cosine similarity algorithm.  Preprocessing dataset, context word dataset and bigram dataset are used to measure the similarity. The context word document set measure gives a better similarity than bigram and pre-processing document set.

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35

Chakrabarty, Amiya Amitabh. "Text Data Labelling using Transformer based Sentence Embeddings and Text Similarity for Text Classification." International Journal on Natural Language Computing 11, no. 2 (2022): 1–8. http://dx.doi.org/10.5121/ijnlc.2022.11201.

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This paper demonstrates that a lot of time, cost, and complexities can be saved and avoided that would otherwise be used to label the text data for classification purposes. The AI world realizes the importance of labelled data and its use for various NLP applications. Here, we have labelled and categorized close to 6,000 unlabelled samples into five distinct classes. This labelled dataset was further used for multi-class text classification. Data labelling task using transformer-based sentence embeddings and applying cosine-based text similarity threshold saved close to 20-30 days of human efforts and multiple human validations with 98.4% of classes correctly labelled as per business validation. Text classification results obtained using this AI labelled data fetched accuracy score and F1 score of 90%.

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Pacharne, Ashish, Pramod S. Nair, and Srinivasa Rao D. "TM-SGTD: Text Mining Based Semantic Graph for Text Document Approach for Text Representation." International Journal of Engineering and Technology 9, no. 4 (2017): 2820–27. http://dx.doi.org/10.21817/ijet/2017/v9i4/170904007.

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Amiya, Amitabh Chakrabarty. "TEXT DATA LABELLING USING TRANSFORMER BASED SENTENCE EMBEDDINGS AND TEXT SIMILARITY FOR TEXT CLASSIFICATION." International Journal on Natural Language Computing (IJNLC 11, no. 2 (2022): 8. https://doi.org/10.5281/zenodo.6545006.

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This paper demonstrates that a lot of time, cost, and complexities can be saved and avoided that would otherwise be used to label the text data for classification purposes. The AI world realizes the importance of labelled data and its use for various NLP applications. Here, we have labelled and categorized close to 6,000 unlabelled samples into five distinct classes. This labelled dataset was further used for multi-class text classification. Data labelling task using transformer-based sentence embeddings and applying cosine-based text similarity threshold saved close to 20-30 days of human efforts and multiple human validations with 98.4% of classes correctly labelled as per business validation. Text classification results obtained using this AI labelled data fetched accuracy score and F1 score of 90%.

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Sabol, Vedran, Keith Andrews, Wolfgang Kienreich, and Michael Granitzer. "Text mapping: Visualising unstructured, structured, and time-based text collections." Intelligent Decision Technologies 2, no. 2 (2008): 117–28. http://dx.doi.org/10.3233/idt-2008-2204.

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Ye, Wei, Xiaogang Gong, Quan Zhang, and Xiaoming Ju. "Text Tiling Text Segmentation Based on Hierarchical Dirichlet Process Model." Journal of Physics: Conference Series 1237 (June 2019): 052004. http://dx.doi.org/10.1088/1742-6596/1237/5/052004.

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Yadav, Anurag Kumar, Mukesh Kumar, and Ayonija Pathre. "Implemented Text Rank based Automatic Text Summarization using Keyword Extraction." International Research Journal of Innovations in Engineering and Technology 04, no. 11 (2020): 20–25. http://dx.doi.org/10.47001/irjiet/2020.411003.

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Minaee, Shervin, Nal Kalchbrenner, Erik Cambria, Narjes Nikzad, Meysam Chenaghlu, and Jianfeng Gao. "Deep Learning--based Text Classification." ACM Computing Surveys 54, no. 3 (2021): 1–40. http://dx.doi.org/10.1145/3439726.

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Deep learning--based models have surpassed classical machine learning--based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural language inference. In this article, we provide a comprehensive review of more than 150 deep learning--based models for text classification developed in recent years, and we discuss their technical contributions, similarities, and strengths. We also provide a summary of more than 40 popular datasets widely used for text classification. Finally, we provide a quantitative analysis of the performance of different deep learning models on popular benchmarks, and we discuss future research directions.

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Mohd Ismail, Hamidah, and Faizah Abd Majid. "Intertextuality in Text-based Discussions." Advances in Language and Literary Studies 2, no. 1 (2011): 18–25. http://dx.doi.org/10.7575/aiac.alls.v.2n.1p.18.

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Pathak, Kangkan, Basant Puri, and Ian Treasaden. "Psychiatry: An Evidence-Based Text." Eastern Journal of Psychiatry 13, no. 1-2 (2021): 124. http://dx.doi.org/10.5005/ejp-13-1--2-124.

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Du, Hongcui, and Alexandra List. "Reasoning about text-based evidence." Contemporary Educational Psychology 68 (January 2022): 102038. http://dx.doi.org/10.1016/j.cedpsych.2021.102038.

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Qian, Yili, Chaochao Jia, and Yimei Liu. "Bert-Based Text Keyword Extraction." Journal of Physics: Conference Series 1992, no. 4 (2021): 042077. http://dx.doi.org/10.1088/1742-6596/1992/4/042077.

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Binwahlan, Mohammed Salem, Naomie Salim, and Ladda Suanmali. "Fuzzy Swarm Based Text Summarization." Journal of Computer Science 5, no. 5 (2009): 338–46. http://dx.doi.org/10.3844/jcssp.2009.338.346.

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Alberola, C., and G. V. Cybenko. "Tracking with text-based messages." IEEE Intelligent Systems 14, no. 4 (1999): 70–78. http://dx.doi.org/10.1109/5254.784087.

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Zeng, Biqing, Yihao Peng, Jichen Yang, Peilin Hong, and Junjie Liang. "CPM-based Hierarchical Text Classification." Journal of Artificial Intelligence Research 82 (January 28, 2025): 367–88. https://doi.org/10.1613/jair.1.16943.

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In the field of natural language processing, hierarchical text classification (HTC) has emerged as a critical task for organizing and analyzing large volumes of text data. The previous work of HTC often falls short in fully leveraging the hierarchical structure of labels, resulting in suboptimal performance. In addition, it is difficult to capture nuanced relationships between parent and child classes, leading to inaccurate predictions and insufficient differentiation between sibling classes under the same parent category. This gap underscores the need for approaches that can more effectively integrate and utilize both hierarchical and corpus-specific information to improve HTC performance. To address these issues, Concept-aware Prompt Mechanism (CPM) is proposed for HTC, which leverages concept information embedded within hierarchical labels to enhance the representation of these labels and improve classification accuracy. Specifically, we introduce a concept initialization module that extracts concept features from hierarchical labels and a novel concept prompt template to integrate these features into the classification process. Our experimental results demonstrate that the proposed CPM achieves state-of-the-art performance on two benchmark datasets, improving Micro-F1 and Macro-F1 scores to varying degrees, particularly in datasets with complex label hierarchies.

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Nguyen, Vu H., Hien T. Nguyen, Hieu N. Duong, and Vaclav Snasel. "n-Gram-Based Text Compression." Computational Intelligence and Neuroscience 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/9483646.

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We propose an efficient method for compressing Vietnamese text usingn-gram dictionaries. It has a significant compression ratio in comparison with those of state-of-the-art methods on the same dataset. Given a text, first, the proposed method splits it inton-grams and then encodes them based onn-gram dictionaries. In the encoding phase, we use a sliding window with a size that ranges from bigram to five grams to obtain the best encoding stream. Eachn-gram is encoded by two to four bytes accordingly based on its correspondingn-gram dictionary. We collected 2.5 GB text corpus from some Vietnamese news agencies to buildn-gram dictionaries from unigram to five grams and achieve dictionaries with a size of 12 GB in total. In order to evaluate our method, we collected a testing set of 10 different text files with different sizes. The experimental results indicate that our method achieves compression ratio around 90% and outperforms state-of-the-art methods.

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Greenhalgh, Jack, and Majid Mirmehdi. "Recognizing Text-Based Traffic Signs." IEEE Transactions on Intelligent Transportation Systems 16, no. 3 (2015): 1360–69. http://dx.doi.org/10.1109/tits.2014.2363167.

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Journal articles on the topic 'Text-based'

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