• Proceedings of the KSPS conference
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• 2006.11a
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• pp.71-74
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• 2006

Many works have been done in the field of retrieving audio segments that contain human speeches without captions. To retrieve newly coined words and proper nouns, subwords were commonly used as indexing units in conjunction with query or document expansion. Among them, document expansion with subwords has serious drawback of large computation overhead. Therefore, in this paper, we propose Expected Matching Score based document expansion that effectively reduces computational overhead without much loss in retrieval precisions. Experiments have shown 13.9 times of speed up at the loss of 0.2% in the retrieval precision.

• Journal of applied mathematics & informatics
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• v.41 no.2
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• pp.345-362
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• 2023

Prouhet string morphism has been a well investigated morphism in different studies on combinatorics on words. In this paper we consider Prouhet array morphism for the images of binary picture arrays in terms of Parikh q-matrices. We state the formulae to calculate q-counting scattered subwords of the images of any arrays under this array morphism and also investigate the properties such as q-weak ratio property and commutative property under this array morphism in terms of Parikh q- matrices of arrays.

• Journal of the Korean Mathematical Society
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• v.59 no.1
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• pp.1-12
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• 2022

We introduce the notion of a minimal synchronizing word; that is a synchronizing word whose proper subwords are not synchronized. This has been used to give a new shorter proof for a theorem in [6]. Also, the common synchronized components of a subshift and its derived set have been characterized.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• Journal of The Korean Association of Information Education
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• v.21 no.2
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• pp.161-169
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• 2017

We often use spellcheckers in order to correct the syntactic errors in our documents. However, these computer programs are not enough for elementary school students, because their sentences are not smooth even after correcting the syntactic errors in many cases. In this paper, we introduce an automated method for evaluating the smoothness of two synonymous sentences. This method uses a recurrent neural network to solve the problem of long-term dependencies and exploits subwords to cope with the rare word problem. We trained the recurrent neural network language model based on a monolingual corpus of about two million English sentences. In our experiments, the trained model successfully selected the more smooth sentences for all of nine types of test set. We expect that our approach will help in elementary school writing after being implemented as an application for smart devices.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.6
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• pp.420-435
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• 2001

As multimedia technology has rapidly grown recently, many researches to process multimedia data efficiently using general-purpose processors have been studied. In this paper, we proposed multimedia instructions which can process multimedia data effectively, and suggested a processor architecture for those instructions. The processor was described with Verilog-HDL in the behavioral level and simulated with CADENCE$^{TM}$ tool. Proposed multimedia instructions are total 48 instructions which can be classified into 7 groups. Multimedia data have 64-bit format and are processed as parallel subwords of 8-bit 8 bytes, 16-bit 4 half words or 32-bit 2 words. Modeled processor is developed based on the Integer Unit of SPARC V.9. It has five-stage pipeline RISC architecture with Harvard principle.e.

• Journal of the Korean Society for information Management
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• v.40 no.4
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• pp.307-327
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• 2023

To analyze the impact of word embedding on book titles, this study utilized word embedding models (Word2vec, GloVe, fastText) to generate embedding vectors from book titles. These vectors were then used as classification features for automatic classification. The classifier utilized the k-nearest neighbors (kNN) algorithm, with the categories for automatic classification based on the DDC (Dewey Decimal Classification) main class 300 assigned by libraries to books. In the automatic classification experiment applying word embeddings to book titles, the Skip-gram architectures of Word2vec and fastText showed better results in the automatic classification performance of the kNN classifier compared to the TF-IDF features. In the optimization of various hyperparameters across the three models, the Skip-gram architecture of the fastText model demonstrated overall good performance. Specifically, better performance was observed when using hierarchical softmax and larger embedding dimensions as hyperparameters in this model. From a performance perspective, fastText can generate embeddings for substrings or subwords using the n-gram method, which has been shown to increase recall. The Skip-gram architecture of the Word2vec model generally showed good performance at low dimensions(size 300) and with small sizes of negative sampling (3 or 5).