• Proceedings of the IEEK Conference
• /
• 1998.10a
• /
• pp.1255-1258
• /
• 1998

Hidden Markov Model (HMM) is the most widely used method in speech recognition. In general, HMM parameters are trained to have maximum likelihood (ML) for training data. This method doesn't take account of discrimination to other words. To complement this problem, this paper proposes a word verification method by re-recognition of the recognized word and its similar word using the discriminative function between two words. The similar word is selected by calculating the probability of other words to each HMM. The recognizer haveing discrimination to each word is realized using the weighting to each state and the weighting is calculated by genetic algorithm.

• Journal of Computing Science and Engineering
• /
• v.10 no.4
• /
• pp.103-110
• /
• 2016

Emotion recognition is an important component of affective computing, and is significant in the implementation of natural and friendly human-computer interaction. An effective approach to recognizing emotion from text is based on a machine learning technique, which deals with emotion recognition as a classification problem. However, in emotion recognition, the texts involved are usually very short, leaving a very large, sparse feature space, which decreases the performance of emotion classification. This paper proposes to resolve the problem of feature sparseness, and largely improve the emotion recognition performance from short texts by doing the following: representing short texts with word cluster features, offering a novel word clustering algorithm, and using a new feature weighting scheme. Emotion classification experiments were performed with different features and weighting schemes on a publicly available dataset. The experimental results suggest that the word cluster features and the proposed weighting scheme can partly resolve problems with feature sparseness and emotion recognition performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.1
• /
• pp.254-276
• /
• 2019

Since the amount of information on the internet is growing rapidly, it is not easy for a user to find relevant information for his/her query. To tackle this issue, the researchers are paying much attention to Document Summarization. The key point in any successful document summarizer is a good document representation. The traditional approaches based on word overlapping mostly fail to produce that kind of representation. Word embedding has shown good performance allowing words to match on a semantic level. Naively concatenating word embeddings makes common words dominant which in turn diminish the representation quality. In this paper, we employ word embeddings to improve the weighting schemes for calculating the Latent Semantic Analysis input matrix. Two embedding-based weighting schemes are proposed and then combined to calculate the values of this matrix. They are modified versions of the augment weight and the entropy frequency that combine the strength of traditional weighting schemes and word embedding. The proposed approach is evaluated on three English datasets, DUC 2002, DUC 2004 and Multilingual 2015 Single-document Summarization. Experimental results on the three datasets show that the proposed model achieved competitive performance compared to the state-of-the-art leading to a conclusion that it provides a better document representation and a better document summary as a result.

• Journal of Information Management
• /
• v.42 no.2
• /
• pp.193-210
• /
• 2011

The study was applied to context window sizes and weighting method to obtain the best performance of word sense disambiguation using support vector machine. The context window sizes were used to a 3-word, sentence, 50-bytes, and document window around the targeted word. The weighting methods were used to Binary, Term Frequency(TF), TF ${\times}$ Inverse Document Frequency(IDF), and Log TF ${\times}$ IDF. As a result, the performance of 50-bytes in the context window size was best. The Binary weighting method showed the best performance.

• MALSORI
• /
• no.46
• /
• pp.117-126
• /
• 2003

This paper presents our language model adaptation for Korean spontaneous speech recognition. Korean spontaneous speech is observed various characteristics of content and style such as filled pauses, word omission, and contraction as compared with the written text corpus. Our approaches focus on improving the estimation of domain-dependent n-gram models by relevance weighting out-of-domain text data, where style is represented by n-gram based tf/sup */idf similarity. In addition to relevance weighting, we use disfluencies as Predictor to the neighboring words. The best result reduces 9.7% word error rate relatively and shows that n-gram based relevance weighting reflects style difference greatly and disfluencies are good predictor also.

• Proceedings of the KSPS conference
• /
• 2003.05a
• /
• pp.83-86
• /
• 2003

This paper presents our style-based language model adaptation for Korean conversational speech recognition. Korean conversational speech is observed various characteristics of content and style such as filled pauses, word omission, and contraction as compared with the written text corpora. For style-based language model adaptation, we report two approaches. Our approaches focus on improving the estimation of domain-dependent n-gram models by relevance weighting out-of-domain text data, where style is represented by n-gram based tf*idf similarity. In addition to relevance weighting, we use disfluencies as predictor to the neighboring words. The best result reduces 6.5％ word error rate absolutely and shows that n-gram based relevance weighting reflects style difference greatly and disfluencies are good predictor.

• Journal of the Korean Society for information Management
• /
• v.1 no.1
• /
• pp.43-62
• /
• 1984

The present study has two main objectives. The first objective is to devise a new term weighting technique which can be used to weight the significance value of each word stem in a test collection of documents on the subject of "enteral hyperalimentation." The next objective is to evaluate retrieval performance of proposed term weighting technique, together with four other term weighting techniques, by conducting a set of experiments. The experimental results have shown that the performance of Sparck Jones's inverse document frequency weighting and the proposed term significance weighting techniques produced better recall and precision ratios than the other three complex weighting techniques.

• Journal of the Korean Society for information Management
• /
• v.4 no.1
• /
• pp.47-86
• /
• 1987

The purpose of this study is to present an effective automatic indexing method of Korean texts based on statistical criteria. Titles and abstracts of the 299 documents randomly selected from ETRI's DOCUMENT data base are used as the experimental data in this study the experimental data is divided into 4 word groups and these 4 word groups are respectively analyzed and evaluated by applying 3 automatic indexing methods including Transition Phenomena of Word Occurrence, Inverse Document Frequency Weighting Technique, and Term Discrimination Weighting Technique.

• Proceedings of the KIEE Conference
• /
• 1988.07a
• /
• pp.179-182
• /
• 1988

Since the bits in any encoded PCM word are of different importance to the bit positions, in order to improve the signal to noise ratio the technique that the encoded signal bits are weighted for the QPSK transmission system, is presented. Also the erasure zone is established at the detector, such that if the output falls into the erasure zone, the regenerated sample is replaced by interpolation. Two weighting methods are shown here. One is the method that the same weighting profile is used to Q and I dimension in QPSK signal constellations. The other is diferent weighting to Q and I dimension. The gains of this new technique in overall signal s/n compared to conventional QPSK transmission system were 5 db and 2db, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.2
• /
• pp.347-354
• /
• 2013

In this paper, we propose a enhancing document clustering method using term re-weighting by the expanded term. The proposed method extracts the important terms of documents in cluster using semantic features, which it can well represent the topics of document to expand term using WordNet. Besides, the method can improve the performance of document clustering using re-weighting terms based on the expanded terms. The experimental results demonstrate appling the proposed method to document clustering methods achieves better performance than the normal document clustering methods.