• The Journal of the Korea Contents Association
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• v.12 no.9
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• pp.9-15
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• 2012

A conventional event sentence extraction research doesn't learn the 3W features in the learning step and applies the rule on whether the 3W feature exists in the extraction step. This paper presents a sentence weight based event sentence extraction method that calculates the weight of the 3W features in the learning step and applies the weight of the 3W features in the extraction step. In the experimental result, we show that top 30% features by the $TF{\times}IDF$ weighting method is good in the feature filtering. In the real estate domain of the public issue, the performance of sentence weight based event sentence extraction method is improved by who and when of 3W features. Moreover, In the real estate domain of the public issue, the sentence weight based event sentence extraction method is better than the other machine learning based extraction method.

• Smart Media Journal
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• v.11 no.3
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• pp.31-38
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• 2022

In the document extraction summary study, various methods for selecting important sentences based on the relationship between sentences were proposed. In the Korean document summary using the summation similarity of sentences, the summation similarity of the sentences was regarded as the amount of sentence information, and the summary sentences were extracted by selecting important sentences based on this. However, the problem is that it does not take into account the various importance that each sentence contributes to the entire document. Therefore, in this study, we propose a document extraction summary method that provides a summary by selecting important sentences based on the amount of quantitative and semantic information in the sentence. As a result, the extracted sentence agreement was 58.56% and the ROUGE-L score was 34, which was superior to the method using only the combined similarity. Compared to the deep learning-based method, the extraction method is lighter, but the performance is similar. Through this, it was confirmed that the method of compressing information based on semantic similarity between sentences is an important approach in document extraction summary. In addition, based on the quickly extracted summary, the document generation summary step can be effectively performed.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.305-320
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• 2009

The news pages provided through the web contain unnecessary information. This causes low performance and inefficiency of the news processing system. In this study, news content extraction methods, which are based on sentence identification and block-level tags news web pages, was suggested. To obtain optimal performance, combinations of these methods were applied. The results showed good performance when using an extraction method which applied the sentence identification and eliminated hyperlink text from web pages. Moreover, this method showed better results when combined with the extraction method which used block-level. Extraction methods, which used sentence identification, were effective for raising the extraction recall ratio.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.76-85
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• 2007

This paper addresses a task of opinion extraction from given documents and its positive/negative classification. We propose a sentence classification method using a notion of syntactic piece. Syntactic piece is a minimum unit of structure, and is used as an alternative processing unit of n-gram and whole tree structure. We compute its semantic orientation, and classify opinion sentences into positive or negative. We have conducted an experiment on more than 5000 opinion sentences of multiple domains, and have proven that our approach attains high performance at 91% precision.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.203-206
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• 2002

In this paper, we present a sentence speech recognizer for twenty questions game. The proposed approaches for speaker-independent sentence speech recognition can be divided into two steps. One is extraction of the number of syllables in eojeol for candidate reduction, and the other is knowledge based language model for sentence recognition. For twenty questions game, we implemented speech recognizer using 956 sentences and 1095 eojeols. The results obtained in our experiments were 87% sentence recognition rate and 90.15% eojeol recognition rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1639-1658
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• 2019

Named entity recognition (NER) is an important technique for improving the performance of data mining and big data analytics. In previous studies, NER systems have been employed to identify named-entities using statistical methods based on prior information or linguistic features; however, such methods are limited in that they are unable to recognize unregistered or unlearned objects. In this paper, a method is proposed to extract objects, such as technologies, theories, or person names, by analyzing the collocation relationship between certain words that simultaneously appear around specific words in the abstracts of academic journals. The method is executed as follows. First, the data is preprocessed using data cleaning and sentence detection to separate the text into single sentences. Then, part-of-speech (POS) tagging is applied to the individual sentences. After this, the appearance and collocation information of the other POS tags is analyzed, excluding the entity candidates, such as nouns. Finally, an entity recognition model is created based on analyzing and classifying the information in the sentences.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.329-336
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• 2006

In this paper, we propose a method of significant term extraction within a document. The technique used is Principal Component Analysis(PCA) which is one of the multivariate analysis methods. PCA can sufficiently use term-term relationships within a document by term-term correlations. We use a correlation matrix instead of a covariance matrix between terms for performing PCA. We also try to find out thresholds of both the number of components to be selected and correlation coefficients between selected components and terms. The experimental results on 283 Korean newspaper articles show that the condition of the first six components with correlation coefficients of |0.4| is the best for extracting sentence based on the significant selected terms.

• Smart Media Journal
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• v.10 no.1
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• pp.63-69
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• 2021

When a person sees a sentence and understands the sentence, the person understands the sentence by reminiscent of the main word in the sentence as an image. Text-to-image is what allows computers to do this associative process. The previous deep learning-based text-to-image model extracts text features using Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) and bi-directional LSTM, and generates an image by inputting it to the GAN. The previous text-to-image model uses basic embedding in text feature extraction, and it takes a long time to train because images are generated using several modules. Therefore, in this research, we propose a method of extracting features by using the attention mechanism, which has improved performance in the natural language processing field, for sentence embedding, and generating an image by inputting the extracted features into the GAN. As a result of the experiment, the inception score was higher than that of the model used in the previous study, and when judged with the naked eye, an image that expresses the features well in the input sentence was created. In addition, even when a long sentence is input, an image that expresses the sentence well was created.

• Journal of Internet Computing and Services
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• v.14 no.3
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• pp.81-90
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• 2013

Mobile device can get useful user information, because users have always this device. In this paper, we propose automatically story generation method and user topic extraction using user information in mobile environment. Proposed method is follows: (1) We collect user action information in mobile device. Then, (2) we extract topics from collected information. (3) For the results of (2), we determine episodes for one day. Then, (4) we generate sentences using sentence templates and we compose stories which have theme-based or time-based. Because proposed method is simpler than previous method, proposed method can work only in mobile device. There's no room to leak user information. And proposed method is expressed more informative than previous method, because proposed method is provided sentence-based result. Extracted user-topic, a result of our method, can use to analyze user action and user preference.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.285-292
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• 2007

The parallel corpus is an important resource in the research field of data-driven natural language processing, but there are only a few parallel corpora publicly available nowadays, mostly due to the high labor force needed to construct this kind of resource. A novel strategy is brought out to automatically fetch parallel text from the web in this paper, which may help to solve the problem of the lack of parallel corpora with high quality. The system we develop first downloads the web pages from certain hosts. Then candidate parallel page pairs are prepared from the page set based on the outer features of the web pages. The candidate page pairs are evaluated in the last step in which the sentences in the candidate web page pairs are extracted and aligned first, and then the similarity of the two web pages is evaluate based on the similarities of the aligned sentences. The experiments towards a multilingual web site show the satisfactory performance of the system.