• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1778-1799
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• 2022

Automatic text summarization is a procedure that packs enormous content into a more limited book that incorporates significant data. Malayalam is one of the toughest languages utilized in certain areas of India, most normally in Kerala and in Lakshadweep. Natural language processing in the Malayalam language is relatively low due to the complexity of the language as well as the scarcity of available resources. In this paper, a way is proposed to deal with the text summarization process in Malayalam documents by training a model based on the Support Vector Machine classification algorithm. Different features of the text are taken into account for training the machine so that the system can output the most important data from the input text. The classifier can classify the most important, important, average, and least significant sentences into separate classes and based on this, the machine will be able to create a summary of the input document. The user can select a compression ratio so that the system will output that much fraction of the summary. The model performance is measured by using different genres of Malayalam documents as well as documents from the same domain. The model is evaluated by considering content evaluation measures precision, recall, F score, and relative utility. Obtained precision and recall value shows that the model is trustable and found to be more relevant compared to the other summarizers.

• 영어어문교육
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• 제17권4호
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• pp.49-74
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• 2011

The present paper aims to explore the role of non-negotiated input and output in language acquisition in the context of free Web chat. In order to examine how input and output contribute to language acquisition, with or without meaning negotiation, the present study examined a Korean EFL learner's chat data collected over 6 months. Chat texts across 43 chat sessions were analyzed, along with her comment notes and interviews. The input and output negotiated for meaning were traced throughout all sessions to find evidence that they were linked to acquisition. Other input and output in the interaction were also traced to ascertain if they contributed to acquisition. The chat text analysis, comment notes, and the interviews revealed that the opportunities of meaning negotiation in a free Web chat context was quite limited and that the learner acquired language even in the absence of meaning negotiation. The findings suggest that input and output via Web chat, whether negotiated or non-negotiated, play their respective roles, contributing to different aspects of acquisition.

• 대한음성학회지:말소리
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• 제56호
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• pp.207-223
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• 2005

The goal of language model adaptation is to improve the background language model with a relatively small adaptation corpus. This study presents a language model adaptation technique where additional text data for the adaptation do not exist. We propose the information retrieval (IR) technique with N-gram language modeling to collect the adaptation corpus from baseline text data. We also propose to use a dynamic language model interpolation coefficient to combine the background language model and the adapted language model. The interpolation coefficient is estimated from the word hypotheses obtained by segmenting the input speech data reserved for held-out validation data. This allows the final adapted model to improve the performance of the background model consistently The proposed approach reduces the word error rate by $13.6\%$ relative to baseline 4-gram for two-hour broadcast news speech recognition.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.93-96
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• 2022

Natural language processing (NLP) is utilized to understand a natural text. Text analysis systems use natural language algorithms to find the meaning of large amounts of text. Text classification represents a basic task of NLP with a wide range of applications such as topic labeling, sentiment analysis, spam detection, and intent detection. The algorithm can transform user's unstructured thoughts into more structured data. In this work, a text classifier has been developed that uses academic admission and registration texts as input, analyzes its content, and then automatically assigns relevant tags such as admission, graduate school, and registration. In this work, the well-known algorithms support vector machine SVM and K-nearest neighbor (kNN) algorithms are used to develop the above-mentioned classifier. The obtained results showed that the SVM classifier outperformed the kNN classifier with an overall accuracy of 98.9%. in addition, the mean absolute error of SVM was 0.0064 while it was 0.0098 for kNN classifier. Based on the obtained results, the SVM is used to implement the academic text classification in this work.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.405-414
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• 2007

This paper describes a preliminary work on prosody modeling aspect of a text-to-speech system for Thai. Specifically, the model is designed to predict symbolic markers from text (i.e., prosodic phrase boundaries, accent, and intonation boundaries), and then using these markers to generate pitch, intensity, and durational patterns for the synthesis module of the system. In this paper, a novel method for annotating the prosodic structure of Thai sentences based on dependency representation of syntax is presented. The goal of the annotation process is to predict from text the rhythm of the input sentence when spoken according to its intended meaning. The encoding of the prosodic structure is established by minimizing speech disrhythmy while maintaining the congruency with syntax. That is, each word in the sentence is assigned a prosodic feature called strength dynamic which is based on the dependency representation of syntax. The strength dynamics assigned are then used to obtain rhythmic groupings in terms of a phonological unit called foot. Finally, the foot structure is used to predict the durational pattern of the input sentence. The aforementioned process has been tested on a set of ambiguous sentences, which represents various structural ambiguities involving five types of compounds in Thai.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권4호
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• pp.1409-1425
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• 2021

In wide-ranging information society, fast and easy access to information in language of one's choice is indispensable, which may be provided by using various multilingual Natural Language Processing (NLP) applications. Natural language text contains references among different language elements, called anaphoric links. Resolving anaphoric links is a key problem in NLP. Anaphora resolution is an essential part of NLP applications. Anaphoric links need to be properly interpreted for clear understanding of natural languages. For this purpose, a mechanism is desirable for the identification and resolution of these naturally occurring anaphoric links. In this paper, a framework based on Hobbs syntactic approach and a system developed by Lappin & Leass is proposed for resolution of reflexive anaphoric links, present in Urdu text documents. Generally, anaphora resolution process takes three main steps: identification of the anaphor, location of the candidate antecedent(s) and selection of the appropriate antecedent. The proposed framework is based on exploring the syntactic structure of reflexive anaphors to find out various features for constructing heuristic rules to develop an algorithm for resolving these anaphoric references. System takes Urdu text containing reflexive anaphors as input, and outputs Urdu text with resolved reflexive anaphoric links. Despite having scarcity of Urdu resources, our results are encouraging. The proposed framework can be utilized in multilingual NLP (m-NLP) applications.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2009년도 IWAIT
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• pp.799-802
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• 2009

We have been studying the next generation of video creation solution based on TVML (TV program Making Language) technology. TVML is a well-known scripting language for computer animation and a TVML Player interprets the script to create video content using real-time 3DCG and synthesized voices. TVML has a long history proposed back in 1996 by NHK, however, the only available Player has been the one made by NHK for years. We have developed a new TVML Player from scratch and named it T2V (Text-To-Vision) Player. Due to the development from scratch, the code is compact, light and fast, and extendable and portable. Moreover, the new T2V Player performs not only a playback of TVML script but also a Text-To-Vision conversion from input written in XML format or just a mere plane text to videos by using 'Text-filter' that can be added as a plug-in of the Player. We plan to make it public as freeware from early 2009 in order to stimulate User-Generated-Content and a various kinds of services running on the Internet and media industry. We think that our T2V Player would be a key technology for upcoming new movement.

• Journal of information and communication convergence engineering
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• 제22권1호
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• pp.33-43
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• 2024

Automated text summarization (ATS) systems rely on language resources as datasets. However, creating these datasets is a complex and labor-intensive task requiring linguists to extensively annotate the data. Consequently, certain public datasets for ATS, particularly in languages such as Thai, are not as readily available as those for the more popular languages. The primary objective of the ATS approach is to condense large volumes of text into shorter summaries, thereby reducing the time required to extract information from extensive textual data. Owing to the challenges involved in preparing language resources, publicly accessible datasets for Thai ATS are relatively scarce compared to those for widely used languages. The goal is to produce concise summaries and accelerate the information extraction process using vast amounts of textual input. This study introduced ThEconSum, an ATS architecture specifically designed for Thai language, using economy-related data. An evaluation of this research revealed the significant remaining tasks and limitations of the Thai language.

• 한국조경학회지
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• 제27권1호
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• pp.54-63
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• 1999

The Landscape drawing is used as main media in landscape design process like the language in daily life for human. Designers input many intentions and meaningful words in design process through landscape drawing. The common purpose of landscape drawing is to represent reality effectively, even though it has variable visual forms and materiality. The representation in landscape drawing in metaphorical as well as visual and functional. But current tendency is inclined to use landscape drawing in a functional aspect for visual representation and the landscape drawing is utilized straight-forwardly rather than metaphorically for clear communication. Such recognition on landscape drawing results from the difficulty to accept the symbolic aspect of the drawing. The difficulty makes the utilization and the interpretation of landscape drawing stay at conventional level in following visible factors. For the sake of solving the difficulty this study considers landscape drawing as the text that contains readable objects and symbolic words. This study presents layer-methods for reading a landscape drawing as a text; situational and contextural reading, iconological reading and reading the subject of drawing.

• 스마트미디어저널
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• 제10권1호
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• pp.63-69
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• 2021

사람이 어떤 문장을 보고 그 문장에 대해 이해하는 것은 문장 안에서 주요한 단어를 이미지로 연상시켜 그 문장에 대해 이해한다. 이러한 연상과정을 컴퓨터가 할 수 있도록 하는 것을 text-to-image라고 한다. 기존 딥 러닝 기반 text-to-image 모델은 Convolutional Neural Network(CNN)-Long Short Term Memory(LSTM), bi-directional LSTM을 사용하여 텍스트의 특징을 추출하고, GAN에 입력으로 하여 이미지를 생성한다. 기존 text-to-image 모델은 텍스트 특징 추출에서 기본적인 임베딩을 사용하였으며, 여러 모듈을 사용하여 이미지를 생성하므로 학습 시간이 오래 걸린다. 따라서 본 연구에서는 자연어 처리분야에서 성능 향상을 보인 어텐션 메커니즘(Attention Mechanism)을 문장 임베딩에 사용하여 특징을 추출하고, 추출된 특징을 GAN에 입력하여 이미지를 생성하는 방법을 제안한다. 실험 결과 기존 연구에서 사용되는 모델보다 inception score가 높았으며 육안으로 판단하였을 때 입력된 문장에서 특징을 잘 표현하는 이미지를 생성하였다. 또한, 긴 문장이 입력되었을 때에도 문장을 잘 표현하는 이미지를 생성하였다.