• The Journal of the Acoustical Society of Korea
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• v.36 no.6
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• pp.436-441
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• 2017

Computing music similarity is indispensable in constructing music retrieval system. This paper focuses on the cover song search among various music-retrieval tasks. We investigate the cover song search method based on the chroma n-gram to reduce storage for feature DB and enhance search accuracy. Specifically we propose t-tab n-gram, n-gram selection method, and n-gram set comparison method. Experiments on the widely used music dataset confirmed that the proposed method improves cover song search accuracy as well as reduces feature storage.

• The Journal of Korean Association of Computer Education
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• v.13 no.6
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• pp.79-89
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• 2010

In this paper, we proposed a method to find out similar sentences from documents to detect plagiarized documents. The proposed model adapts LSA and N-gram techniques to detect every type of Korean plagiarized sentence type. To evaluate the performance of the model, we constructed experimental data using students' essays on the same theme. Students made their essay by intentionally plagiarizing some reference documents. The experimental results showed that our proposed model outperforms the conventional N-gram model, Vector model, LSA model in precision, recall, and F measures.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.402-404
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• 1998

최근 영상 정보를 저장하는 시스템의 급증으로, 영상의 특징 요소들의 유사성(similarity)에 근거하여 영상을 분류.검색하는 기술에 많은 관심을 보이고 있다. 본 논문에서는 칼라영상의 분류를 위해 기존의 N$\times$M-grams를 변형한 Color N$\times$M-grams를 제안한다. Color N$\times$M-grams는 영상의 칼라정보를 이용하여 영상고유의 구조 정보를 추출한 후 유사성을 측정하여 영상을 분류한다. 제안된 방법의 성능 평가를 위하여 39쌍의 Benchmark 영상을 사용하여 실험하였다. 실험결과, 제안한 Color N$\times$M-grams를 사용한 방법이 기존의 N$\times$M-grams를 사용하여 칼라 영상을 분류하는 방법보다 1순위로 분류되는 비율에 있어서 약 19% 더 좋은 결과를 보였다.

• MALSORI
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• no.46
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• pp.117-126
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• 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.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.711-716
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• 2014

With the gradual increase of interest in plagiarism and intelligent file content search, the demand for similarity measuring between two sentences is increasing. There is a lot of researches for sentence similarity measurement methods in various directions such as n-gram, edit-distance and LSA. However, these methods have their own advantages and disadvantages. In this paper, we propose a new sentence similarity measurement method approaching from another direction. The proposed method uses the set-based POI data search that improves search performance compared to the existing hard matching method when data includes the inverse, omission, insertion and revision of characters. Using this method, we are able to measure the similarity between two sentences more accurately and more quickly. We modified the data loading and text search algorithm of the set-based POI data search. We also added a word operation algorithm and a similarity measure between two sentences expressed as a percentage. From the experimental results, we observe that our sentence similarity measurement method shows better performance than n-gram and the set-based POI data search.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.12-16
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• 2007

The function of a protein is closely co-related with its subcellular location(s). Given a protein sequence, therefore, how to determine its subcellular location is a vitally important problem. We have developed a new prediction method for protein subcellular location(s), which is based on n-gram feature extraction and k-nearest neighbor (kNN) classification algorithm. It classifies a protein sequence to one or more subcellular compartments based on the locations of top k sequences which show the highest similarity weights against the input sequence. The similarity weight is a kind of similarity measure which is determined by comparing n-gram features between two sequences. Currently our method extract penta-grams as features of protein sequences, computes scores of the potential localization site(s) using kNN algorithm, and finally presents the locations and their associated scores. We constructed a large-scale data set of protein sequences with known subcellular locations from the SWISS-PROT database. This data set contains 51,885 entries with one or more known subcellular locations. Our method show very high prediction precision of about 93% for this data set, and compared with other method, it also showed comparable prediction improvement for a test collection used in a previous work.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04b
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• pp.324-326
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• 2000

본 논문에서는 항공편 예약이라는 제한 영역에서의 연속음성인식 시스템을 위한 후처리 본 논문에서는 항공편 예약이라는 제한 영역에서의 연속음성인식 시스템을 위한 후처리 방안을 제시한다. 제안하는 후처리 방안은 200 문장의 항공편 예약 텍스트 데이터를 이용하여 문형 정보를 추출한 뒤 특정 문형별로 분류하였다. 분류된 문형과 음성인식 후의 문장을 비교하여 가장 유사한 문형을 추론한다. 추론한 특정 문형에서 나올 수 있는 형태소를 형태소들간의 N-gram 정보가 수록된 데이터베이스를 이용하여 형태소를 수정하고 보완한 결과를 최종 문장으로 출력한다.

• Informatization Policy
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• v.26 no.4
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• pp.85-106
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• 2019

Recently, there have been increasing cases of collective petitions filed in the electronic petitions system. However, there is no efficient management system, raising concerns on side effects such as increased administrative workload and mass production of social conflicts. Aimed at suggesting a methodology for estimating electronic collective petitions using anomaly detection and corpus linguistics-based content analysis, this study conducted the followings: i) a theoretical review of the concept of collective petitions, ii) estimation of electronic collective petitions using anomaly detection based on nonparametric unsupervised learning, iii) a content similarity analysis on petitions using n-gram cosine angle distance, and iv) a case study on the Voice of Citizens of Changwon City, through which the utility of the proposed methodology, policy implications and future tasks were reviewed.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.9
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• pp.445-456
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• 2017

In this paper, we propose a method to detect spam tweets containing unhealthy information by using an n-gram dictionary under limited labeling. Spam tweets that contain unhealthy information have a tendency to use similar words and sentences. Based on this characteristic, we show that spam tweets can be effectively detected by applying a Naive Bayesian classifier using n-gram dictionaries which are constructed from spam tweets and normal tweets. On the other hand, constructing an initial training set requires very high cost because a large amount of data flows in real time in a twitter. Therefore, there is a need for a spam detection method that can be applied in an environment where the initial training set is very small or non exist. To solve the problem, we propose a method to generate pseudo-labels by utilizing twitter's retweet function and use them for the configuration of the initial training set and the n-gram dictionary update. The results from various experiments using 1.3 million korean tweets collected from December 1, 2016 to December 7, 2016 prove that the proposed method has superior performance than the compared spam detection methods.

• Journal of Software Assessment and Valuation
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• v.12 no.2
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• pp.1-8
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• 2016

In this paper, we study abstracting and identifying license file from a package to prevent unintentional intellectual property infringement because of lost/modified/confliction of license information when redistributing open source software. To invest character of the license files, we analyzed 322 licenses by n-gram and TF-IDF methods, and abstract license files from the packages. We identified license information with a similarity of the registered licenses by cosine measurement.