• Title/Summary/Keyword: word vector

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The Design of Optimal Filters in Vector-Quantized Subband Codecs (벡터양자화된 부대역 코덱에서 최적필터의 구현)

  • 지인호
    • The Journal of the Acoustical Society of Korea
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    • v.19 no.1
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    • pp.97-102
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    • 2000
  • Subband coding is to divide the signal frequency band into a set of uncorrelated frequency bands by filtering and then to encode each of these subbands using a bit allocation rationale matched to the signal energy in that subband. The actual coding of the subband signal can be done using waveform encoding techniques such as PCM, DPCM and vector quantizer(VQ) in order to obtain higher data compression. Most researchers have focused on the error in the quantizer, but not on the overall reconstruction error and its dependence on the filter bank. This paper provides a thorough analysis of subband codecs and further development of optimum filter bank design using vector quantizer. We compute the mean squared reconstruction error(MSE) which depends on N the number of entries in each code book, k the length of each code word, and on the filter bank coefficients. We form this MSE measure in terms of the equivalent quantization model and find the optimum FIR filter coefficients for each channel in the M-band structure for a given bit rate, given filter length, and given input signal correlation model. Specific design examples are worked out for 4-tap filter in 2-band paraunitary filter bank structure. These optimum paraunitary filter coefficients are obtained by using Monte Carlo simulation. We expect that the results of this work could be contributed to study on the optimum design of subband codecs using vector quantizer.

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A Semi-Automatic Semantic Mark Tagging System for Building Dialogue Corpus (대화 말뭉치 구축을 위한 반자동 의미표지 태깅 시스템)

  • Park, Junhyeok;Lee, Songwook;Lim, Yoonseob;Choi, Jongsuk
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.5
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    • pp.213-222
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    • 2019
  • Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.

A Document Sentiment Classification System Based on the Feature Weighting Method Improved by Measuring Sentence Sentiment Intensity (문장 감정 강도를 반영한 개선된 자질 가중치 기법 기반의 문서 감정 분류 시스템)

  • Hwang, Jae-Won;Ko, Young-Joong
    • Journal of KIISE:Software and Applications
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    • v.36 no.6
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    • pp.491-497
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    • 2009
  • This paper proposes a new feature weighting method for document sentiment classification. The proposed method considers the difference of sentiment intensities among sentences in a document. Sentiment features consist of sentiment vocabulary words and the sentiment intensity scores of them are estimated by the chi-square statistics. Sentiment intensity of each sentence can be measured by using the obtained chi-square statistics value of each sentiment feature. The calculated intensity values of each sentence are finally applied to the TF-IDF weighting method for whole features in the document. In this paper, we evaluate the proposed method using support vector machine. Our experimental results show that the proposed method performs about 2.0% better than the baseline which doesn't consider the sentiment intensity of a sentence.

Documentation of Printed Hangul Images of the Selected Area by Finger Movement (손가락 이동에 의해 선택된 영역의 인쇄체 한글 영상 문서화)

  • Beak, Seung-Bok
    • Journal of the Korean Institute of Intelligent Systems
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    • v.12 no.4
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    • pp.306-310
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    • 2002
  • In this paper, we realized a system that converts the Korean alphabet (Hangul) images, which are in any domain that is formed by the finger movement on the Hangul document, to the editable characters and then outputs them to the word editor. The domain of hand is separated from the sphere of document in the pre-process step of image. The centroid point of hand is drawn by the maximum circular movement method. After the system recognizes the hand with the circular pattern vector algorithm, finds out the position of finger by the distance spectrum and then draws out the sphere of selected character image by the finger movement to divide the characters into character units by applying the histogram between the Hangul characters. We standardized the characters of various sizes. We used the circular pattern vector algorithm that grafts on the fuzzy inference to divert the character images of the domain, which user wants, to the editable characters by comparing the characteristic vectors between the standard pattern character and the inputted character and by recognizing the character.

Multi-class Support Vector Machines Model Based Clustering for Hierarchical Document Categorization in Big Data Environment (빅 데이터 환경에서 계층적 문서 유형 분류를 위한 클러스터링 기반 다중 SVM 모델)

  • Kim, Young Soo;Lee, Byoung Yup
    • The Journal of the Korea Contents Association
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    • v.17 no.11
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    • pp.600-608
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    • 2017
  • Recently data growth rates are growing exponentially according to the rapid expansion of internet. Since users need some of all the information, they carry a heavy workload for examination and discovery of the necessary contents. Therefore information retrieval must provide hierarchical class information and the priority of examination through the evaluation of similarity on query and documents. In this paper we propose an Multi-class support vector machines model based clustering for hierarchical document categorization that make semantic search possible considering the word co-occurrence measures. A combination of hierarchical document categorization and SVM classifier gives high performance for analytical classification of web documents that increase exponentially according to extension of document hierarchy. More information retrieval systems are expected to use our proposed model in their developments and can perform a accurate and rapid information retrieval service.

A Sentence Sentiment Classification reflecting Formal and Informal Vocabulary Information (형식적 및 비형식적 어휘 정보를 반영한 문장 감정 분류)

  • Cho, Sang-Hyun;Kang, Hang-Bong
    • The KIPS Transactions:PartB
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    • v.18B no.5
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    • pp.325-332
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    • 2011
  • Social Network Services(SNS) such as Twitter, Facebook and Myspace have gained popularity worldwide. Especially, sentiment analysis of SNS users' sentence is very important since it is very useful in the opinion mining. In this paper, we propose a new sentiment classification method of sentences which contains formal and informal vocabulary such as emoticons, and newly coined words. Previous methods used only formal vocabulary to classify sentiments of sentences. However, these methods are not quite effective because internet users use sentences that contain informal vocabulary. In addition, we construct suggest to construct domain sentiment vocabulary because the same word may represent different sentiments in different domains. Feature vectors are extracted from the sentiment vocabulary information and classified by Support Vector Machine(SVM). Our proposed method shows good performance in classification accuracy.

Classification of ratings in online reviews (온라인 리뷰에서 평점의 분류)

  • Choi, Dongjun;Choi, Hosik;Park, Changyi
    • Journal of the Korean Data and Information Science Society
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    • v.27 no.4
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    • pp.845-854
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    • 2016
  • Sentiment analysis or opinion mining is a technique of text mining employed to identify subjective information or opinions of an individual from documents in blogs, reviews, articles, or social networks. In the literature, only a problem of binary classification of ratings based on review texts in an online review. However, because there can be positive or negative reviews as well as neutral reviews, a multi-class classification will be more appropriate than the binary classification. To this end, we consider the multi-class classification of ratings based on review texts. In the preprocessing stage, we extract words related with ratings using chi-square statistic. Then the extracted words are used as input variables to multi-class classifiers such as support vector machines and proportional odds model to compare their predictive performances.

The Method of Using the Automatic Word Clustering System for the Evaluation of Verbal Lexical-Semantic Network (동사 어휘의미망 평가를 위한 단어클러스터링 시스템의 활용 방안)

  • Kim Hae-Gyung;Yoon Ae-Sun
    • Journal of the Korean Society for Library and Information Science
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    • v.40 no.3
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    • pp.175-190
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    • 2006
  • For the recent several years, there has been much interest in lexical semantic network However it seems to be very difficult to evaluate the effectiveness and correctness of it and invent the methods for applying it into various problem domains. In order to offer the fundamental ideas about how to evaluate and utilize lexical semantic networks, we developed two automatic vol·d clustering systems, which are called system A and system B respectively. 68.455.856 words were used to learn both systems. We compared the clustering results of system A to those of system B which is extended by the lexical-semantic network. The system B is extended by reconstructing the feature vectors which are used the elements of the lexical-semantic network of 3.656 '-ha' verbs. The target data is the 'multilingual Word Net-CoroNet'. When we compared the accuracy of the system A and system B, we found that system B showed the accuracy of 46.6% which is better than that of system A. 45.3%.

Aspect-Based Sentiment Analysis Using BERT: Developing Aspect Category Sentiment Classification Models (BERT를 활용한 속성기반 감성분석: 속성카테고리 감성분류 모델 개발)

  • Park, Hyun-jung;Shin, Kyung-shik
    • Journal of Intelligence and Information Systems
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    • v.26 no.4
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    • pp.1-25
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    • 2020
  • Sentiment Analysis (SA) is a Natural Language Processing (NLP) task that analyzes the sentiments consumers or the public feel about an arbitrary object from written texts. Furthermore, Aspect-Based Sentiment Analysis (ABSA) is a fine-grained analysis of the sentiments towards each aspect of an object. Since having a more practical value in terms of business, ABSA is drawing attention from both academic and industrial organizations. When there is a review that says "The restaurant is expensive but the food is really fantastic", for example, the general SA evaluates the overall sentiment towards the 'restaurant' as 'positive', while ABSA identifies the restaurant's aspect 'price' as 'negative' and 'food' aspect as 'positive'. Thus, ABSA enables a more specific and effective marketing strategy. In order to perform ABSA, it is necessary to identify what are the aspect terms or aspect categories included in the text, and judge the sentiments towards them. Accordingly, there exist four main areas in ABSA; aspect term extraction, aspect category detection, Aspect Term Sentiment Classification (ATSC), and Aspect Category Sentiment Classification (ACSC). It is usually conducted by extracting aspect terms and then performing ATSC to analyze sentiments for the given aspect terms, or by extracting aspect categories and then performing ACSC to analyze sentiments for the given aspect category. Here, an aspect category is expressed in one or more aspect terms, or indirectly inferred by other words. In the preceding example sentence, 'price' and 'food' are both aspect categories, and the aspect category 'food' is expressed by the aspect term 'food' included in the review. If the review sentence includes 'pasta', 'steak', or 'grilled chicken special', these can all be aspect terms for the aspect category 'food'. As such, an aspect category referred to by one or more specific aspect terms is called an explicit aspect. On the other hand, the aspect category like 'price', which does not have any specific aspect terms but can be indirectly guessed with an emotional word 'expensive,' is called an implicit aspect. So far, the 'aspect category' has been used to avoid confusion about 'aspect term'. From now on, we will consider 'aspect category' and 'aspect' as the same concept and use the word 'aspect' more for convenience. And one thing to note is that ATSC analyzes the sentiment towards given aspect terms, so it deals only with explicit aspects, and ACSC treats not only explicit aspects but also implicit aspects. This study seeks to find answers to the following issues ignored in the previous studies when applying the BERT pre-trained language model to ACSC and derives superior ACSC models. First, is it more effective to reflect the output vector of tokens for aspect categories than to use only the final output vector of [CLS] token as a classification vector? Second, is there any performance difference between QA (Question Answering) and NLI (Natural Language Inference) types in the sentence-pair configuration of input data? Third, is there any performance difference according to the order of sentence including aspect category in the QA or NLI type sentence-pair configuration of input data? To achieve these research objectives, we implemented 12 ACSC models and conducted experiments on 4 English benchmark datasets. As a result, ACSC models that provide performance beyond the existing studies without expanding the training dataset were derived. In addition, it was found that it is more effective to reflect the output vector of the aspect category token than to use only the output vector for the [CLS] token as a classification vector. It was also found that QA type input generally provides better performance than NLI, and the order of the sentence with the aspect category in QA type is irrelevant with performance. There may be some differences depending on the characteristics of the dataset, but when using NLI type sentence-pair input, placing the sentence containing the aspect category second seems to provide better performance. The new methodology for designing the ACSC model used in this study could be similarly applied to other studies such as ATSC.

Design and Implementation of 2D Fashion Design System based on Diagraming Piece (도식화 피스기반 2D패션 디자인 시스템의 설계 및 구현)

  • 김영운;이혜정;정성태;정석태;이용주;조진애
    • Journal of the Korea Society of Computer and Information
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    • v.9 no.1
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    • pp.45-54
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    • 2004
  • A CAD and diagraming program of clothing and fashion design field within the country variously exist, but this paper implements design CAD and diagraming system based on diagraming piece with database system. Proposal system can be used quickly and easily to designer because of providing various diagraming piece. intend to utilize the data to diagraming piece provide with various classification and key word. 2D fashion design system provide GUI, various drawing tool, vector type document. application data based on XML, diverse library and easy mapping capacity for user.

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