• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.7-10
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• 2000

Studies of prosody and sentence processing have demonstrated that prosodic phrasing can exhibit strong effects on processing decisions in English. In this paper, we tested Korean sentence fragments containing syntactically ambiguous Adj-N1-N2 strings in a cross-modal naming task. Four accentual phrasing patterns were tested: (a) the default phrasing pattern, in which each word forms an accentual phrase; (b) a phrasing biased toward N1 modification; (c) a phrasing biased toward complex-NP modification; and (d) a phrasing used with adjective focus. Patterns (b) and (c) are disambiguating phrasings; the other two are commonly found with both interpretations and are thus ambiguous. The results showed that the naming time of items produced in the prosody contradicting the semantic grouping is significantly longer than that produced in either default or supporting prosody, We claim that, as in English, prosodic information in Korean is parsed into a well-formed prosodic representation during the early stages of processing. The partially constructed prosodic representation produces incremental effects on syntactic and semantic processing decisions and is retained in memory to influence reanalysis decisions.

• Journal of KIISE:Software and Applications
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• v.35 no.1
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• pp.50-58
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• 2008

Most of Korean sentences are complex sentences which consisted of main clause and embedded clause. These complex sentences have more than one predicate and this causes various syntactic ambiguities in syntactic analysis. These ambiguities are caused by phrase attachment problems which are occurred by the modifying scope of embedded clause. To resolve it, we decide the scope of embedded clause in the sentence and consider this clause as a unit of syntactic category. In this paper, we use sentence patterns information(SPI) and syntactic properties of Korean to decide a scope of embedded clause. First, we split the complex sentence into embedded clause and main clause by the method that embedded clause must have maximal arguments. This work is done by the SPI of the predicate in the embedded clause. And then, the role of this embedded clause is converted into a noun phrases or adverbial phrases in the main clause by the properties of Korean syntax. By this method, the structure of complex sentence is exchanged into a clause. And some phrases attachment problem, which is mainly caused by the modifying scope, is resolved easily. In this paper, we call this method clausal segmentation for embedded clause. By empirical results of parsing 1000 sentences, we found that our method decreases 88.32% of syntactic ambiguities compared to the method that doesn't use SPI and split the sentence with basic clauses.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.236-241
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• 2008

This paper discusses a Korean-English machine translation method using expression patterns. The expression patterns are defined for the purpose of aligning Korean expressions with appropriate English expressions in semantic and expressive senses. This paper also argues to develop a new Korean syntax analysis method using agglutinative characteristics of Korean language, expression pattern concept, sentence partition concept, and incorporation of semantic structures as well in the parsing process. We defined a simple Korean grammar to show the possibility of new Korean syntax analysis method.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.3
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• pp.1-11
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• 2000

An example-based machine translation approach is a new paradigm for resolving various problems caused by the rules of conventional rule-based machine translation. But, in pure example-based machine translation, it is very hard to find similar examples matched with input sentences by using reasonable parallel corpus. This problem causes large overheads in the process of sentence generation. This paper proposes new method of English-Korean transfer using both patterns and examples. The patterns are composed of sentence patterns and phrase patterns. Meta parts of the patterns make the example-based machine translation more practical by raising the probability to find similar examples. The use of patterns and examples can reduce the ambiguities in source language analysis and give us a high quality of MT. And experimental results with a test corpus are discussed.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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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.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.43-52
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• 2017

Pattern Analysis is One of the Most Important Techniques in the Signal and Image Processing and Text Mining Fields. Discrete Fourier Transform (DFT) is Generally Used to Analyzing the Pattern of Signals and Images. We thought DFT could also be used on the Analysis of Text Patterns. In this Paper, DFT is Firstly Adapted in the World to the Sentence Plagiarism Detection Which Detects if Text Patterns of a Document Exist in Other Documents. We Signalize the Texts Converting Texts to ASCII Codes and Apply the Cross-Correlation Method to Detect the Simple Text Plagiarisms such as Cut-and-paste, term Relocations and etc. WordNet is using to find Similarities to Detect the Plagiarism that uses Synonyms, Translations, Summarizations and etc. The Data set, 2013 Corpus, Provided by PAN Which is the One of Well-known Workshops for Text Plagiarism is used in our Experiments. Our Method are Fourth Ranked Among the Eleven most Outstanding Plagiarism Detection Methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1505-1511
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• 2021

Pretrained models with high performance in various tasks within natural language processing have the advantage of learning the linguistic patterns of sentences using large corpus during the training, allowing each token in the input sentence to be represented with appropriate feature vectors. One of the methods of constructing a corpus required for a pre-trained model training is a collection method using web crawler. However, sentences that exist on web may contain unnecessary words in some or all of the sentences because they have various patterns. In this paper, we propose a dataset construction method for filtering sentences containing unnecessary words using neural network models for corpus collected from the web. As a result, we construct a dataset containing a total of 2,330 sentences. We also evaluated the performance of neural network models on the constructed dataset, and the BERT model showed the highest performance with an accuracy of 93.75%.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1254-1271
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• 2018

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

• The Transactions of the Korea Information Processing Society
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• v.7 no.4
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• pp.1150-1159
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• 2000

This paper presents an automatic text categorization model that improves the accuracy by combining statistical and knowledge-based categorization methods. In our model we apply knowledge-based method first, and then apply statistical method on the text which are not categorized by knowledge-based method. By using this combined method, we can improve the accuracy of categorization while categorize all the texts without failure. For statistical categorization, the vector model with Inverted Category Frequency (ICF) weighting is used. For knowledge-based categorization, Phrasal Patterns and Keyword Sets are introduced to represent sentence patterns, and then pattern matching is performed. Experimental results on new articles show that the accuracy of categorization can be improved by combining the tow different categorization methods.

• Speech Sciences
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• v.8 no.2
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• pp.133-148
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• 2001

Focus and information structure are closely related with the distribution of pitch accents. A focused word conveys new information and bears a pitch accent. A content word can usually get a pitch accent, but it can be deaccented if it is mentioned earlier in the discourse. In this paper, we test how English native speakers and Korean learners of English realize pitch accents according to focus and information structure of a sentence. The production experiment shows that English native speakers give a pitch accent to narrow-focused items, deaccenting all the other items of the sentence. For VP broad focus, native speakers give a pitch accent either to both the verb and its complement or to the complement only. On the other hand, it is found that Koreans give pitch accents to most content words regardless of focus and information structure. Moreover, the perception experiment confirms that Koreans' intonation patterns, which are not appropriate in terms of focus and information structure, may jeopardize listeners' comprehension. This paper shows that Korean speakers have little knowledge about focus and information structure for intonational realization, and that such notions should be applied to teaching of English intonation.