• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.271-276
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• 2014

Short message service(SMS) in a mobile communication environment is a very convenient method. However, it caused a serious side effect of generating spam messages for advertisement. Those who send spam messages distort or deform SMS sentences to avoid the messages being filtered by automatic filtering system. In order to increase the performance of spam filtering system, we need to recover the distorted sentences into normal sentences. This paper proposes a method of normalizing the various types of distorted sentence and extracting keywords through automatic word spacing and compound noun decomposition.

• Smart Media Journal
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• v.9 no.4
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• pp.144-151
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• 2020

In a mobile environment, communication takes place via SMS text messages. Vocabularies used in SMS texts can be expected to use vocabularies of different classes from those used in general Korean literary style sentence. For example, in the case of a typical literary style, the sentence is correctly initiated or terminated and the sentence is well constructed, while SMS text corpus often replaces the component with an omission and a brief representation. To analyze these vocabulary usage characteristics, the existing colloquial style corpus and the literary style corpus are used. The experiment compares and analyzes the vocabulary use characteristics of the colloquial corpus SMS text corpus and the Naver Sentiment Movie Corpus, and the written Korean written corpus. For the comparison and analysis of vocabulary for each corpus, the part of speech tag adjective (VA) was used as a standard, and a distinctive collexeme analysis method was used to measure collostructural strength. As a result, it was confirmed that adjectives related to emotional expression such as'good-','sorry-', and'joy-' were preferred in the SMS text corpus, while adjectives related to evaluation expressions were preferred in the Naver Sentiment Movie Corpus. The word embedding was used to automatically construct a sentiment lexicon based on the extracted adjectives with high collostructural strength, and a total of 343,603 sentiment representations were automatically built.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.330-333
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• 2007

This paper presents a dialogue interface using the dialogue management system as a method for controlling home appliances in Home Network Services. In order to realize this type of dialogue interface, we annotated 96,000 utterance pair sized dialogue set and developed an example-based dialogue system. This paper introduces the automatic error correction module for the SMS-styled sentence. With this module we increase the accuracy of NLU(Natural Language Understanding) module. Our NLU module shows an accuracy of 86.2%, which is an improvement of 5.25% over than the baseline. The task completeness of the proposed SMS dialogue interface was 82%.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.682-686
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• 2006

There have been increasing interests in 3D mobile content service with emergence of a terminal equipping with a mobile 3D engine and growth of mobile content market. Mobile 3D Avatar is the most effective product displaying the character of a personalized mobile device user. However, previous studies on the method of expressing 3D Avatar have been mainly focused on natural and realistic expressions according to the change in facial expressions and lip shape of a character in PC based virtual environments. In this paper, we propose a method of synchronizing the lip shape with voice by applying a SMS message received in mobile environments to 3D mobile Avatar. The proposed method enables to realize a natural and effective SMS message reading service of mobile Avatar by disassembling a received message sentence into units of a syllable and then synchronizing the lip shape of 3D Avatar with the corresponding voice.

• Smart Media Journal
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• v.7 no.4
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• pp.24-29
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• 2018

Text analysis technique for natural language processing in deep learning represents words in vector form through word embedding. In this paper, we propose a method of constructing a document vector and classifying it into spam and normal text message, using word embedding and deep learning method. Automatic spacing applied in the preprocessing process ensures that words with similar context are adjacently represented in vector space. Additionally, the intentional word formation errors with non-alphabetic or extraordinary characters are designed to avoid being blocked by spam message filter. Two embedding algorithms, CBOW and skip grams, are used to produce the sentence vector and the performance and the accuracy of deep learning based spam filter model are measured by comparing to those of SVM Light.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.386-391
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• 2008

Some spoken word errors that violate grammatical or writing rules occurs frequently in communication environments like mobile phone and messenger. These unexpected errors cause a problem in a language processing system for many applications like speech recognition, text-to-speech translation, and so on. In this paper, we proposed and implemented an automatic correction system of ill-formed words and word spacing errors in SMS sentences that has been the major errors of poor accuracy. We experimented three methods of constructing the word correction dictionary and evaluated the results of those methods. They are (1) manual construction of error words from the vocabulary list of ill-formed communication languages, (2) automatic construction of error dictionary from the manually constructed corpus, and (3) context-dependent method of automatic construction of error dictionary.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.283-295
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• 2008

With increasing usages of a messenger and a SMS, many young people are habitually using a new-style of sentences with intentionally disjoint Korean syllables. To develop a natural language interface system in these environments, we should first develop a technique that converts a sequence of disjoint Korean syllables to a correct sentence. Therefore, we propose a method to assemble a sequence of disjoint Korean syllables into a correct sentence by using two-step rules. In the first step, the proposed method assembles CVC (consonant-vowel-consonant) forms of simple-disjoint Korean syllables by using manual heuristic rules. In the second step, the proposed method assembles CCVCC forms of double-disjoint Korean syllables by using a mapping table and a transformation-based learning technique. In the experiment, the proposed method showed the perfect precision of 100% in assembling simple-disjoint Korean syllables and the high precision of 99.98% in assembling double-disjoint Korean syllables.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.57-64
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• 2017

Short message service (SMS) is one of the most important communication methods for people who use mobile phones. However, illegal advertising spam messages exploit people because they can be used without the need for friend registration. Recently, spam message filtering systems that use machine learning have been developed, but they have some disadvantages such as requiring many calculations. In this paper, we implemented a spam message filtering system using the set-based POI search algorithm and sentence similarity without servers. This algorithm can judge whether the input query is a spam message or not using only letter composition without any server computing. Therefore, we can filter the spam message although the input text message has been intentionally modified. We added a specific preprocessing option which aims to enable spam filtering. Based on the experimental results, we observe that our spam message filtering system shows better performance than the original set-based POI search algorithm. We evaluate the proposed system through extensive simulation. According to the simulation results, the proposed system can filter the text message and show high accuracy performance against the text message which cannot be filtered by the 3 major telecom companies.