• The Journal of Korean Association of Computer Education
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• v.10 no.3
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• pp.1-8
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• 2007

In this paper, we introduce a new measure called surprisal that estimates the informativeness of transactional instances and attributes in the item response dataset and improve the quality of association rules. In order to this, we set artificial dataset and eliminate noisy and uninformative data using the surprisal first, and then generate association rules between items. And we compare the association rules from the dataset after surprisal-based pruning with support-based pruning and original dataset unpruned. Experimental result that the surprisal-based pruning improves quality of association rules in question item response datasets significantly.

• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.41-49
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• 2019

We are to build an unsupervised machine learning-based language model which can estimate the amount of information that are in need to process words consisting of subword-level morphemes and syllables. We are then to investigate whether the reading times of words reflecting their morphemic and syllabic structures are predicted by an information-theoretic measure such as surprisal. Specifically, the proposed Morfessor-based unsupervised machine learning model is first to be trained on the large dataset of sentences on Sejong Corpus and is then to be applied to estimate the information-theoretic measure on each word in the test data of Korean words. The reading times of the words in the test data are to be recruited from Korean Lexicon Project (KLP) Database. A comparison between the information-theoretic measures of the words in point and the corresponding reading times by using a linear mixed effect model reveals a reliable correlation between surprisal and reading time. We conclude that surprisal is positively related to the processing effort (i.e. reading time), confirming the surprisal hypothesis.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.340-345
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• 2021

최근 많은 연구들이 BERT를 활용하여, 주어진 문맥에서 언어학/문법적으로 적절하지 않은 단어를 인지하고 찾아내는 성과를 보고하였다. 하지만 일반적으로 딥러닝 관점에서 NLL기법(Negative log-likelihood)은 주어진 문맥에서 언어 변칙에 대한 정확한 성격을 규명하기에는 어려움이 있다고 지적되고 있다. 이러한 한계를 해결하기 위하여, Li et al.(2021)은 트랜스포머 언어모델의 은닉층별 밀도 추정(density estimation)을 통한 가우시안 확률 분포를 활용하는 가우시안 혼합 모델(Gaussian Mixture Model)을 적용하였다. 그들은 트랜스포머 언어모델이 언어 변칙 예문들의 종류에 따라 상이한 메커니즘을 사용하여 처리한다는 점을 보고하였다. 이 선행 연구를 받아들여 본 연구에서는 한국어 기반 언어모델인 KoBERT나 KR-BERT도 과연 한국어의 상이한 유형의 언어 변칙 예문들을 다른 방식으로 처리할 수 있는지를 규명하고자 한다. 이를 위해, 본 연구에서는 한국어 형태통사적 그리고 의미적 변칙 예문들을 구성하였고, 이 예문들을 바탕으로 한국어 기반 모델들의 성능을 놀라움-갭(surprisal gap) 점수를 계산하여 평가하였다. 본 논문에서는 한국어 기반 모델들도 의미적 변칙 예문을 처리할 때보다 형태통사적 변칙 예문을 처리할 때 상대적으로 보다 더 높은 놀라움-갭 점수를 보여주고 있음을 발견하였다. 즉, 상이한 종류의 언어 변칙 예문들을 처리하기 위하여 다른 메커니즘을 활용하고 있음을 보였다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.11
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• pp.27-40
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• 2020

In this paper, we investigate the correlation between the amount of English sentences that Korean English learners (L2ers) are exposed to and their sentence processing patterns by examining what Long Short-Term Memory (LSTM) language models (LMs) can learn about implicit syntactic relationship: that is, the filler-gap dependency. The filler-gap dependency refers to a relationship between a (wh-)filler, which is a wh-phrase like 'what' or 'who' overtly in clause-peripheral position, and its gap in clause-internal position, which is an invisible, empty syntactic position to be filled by the (wh-)filler for proper interpretation. Here to implement L2ers' English learning, we build LSTM LMs that in turn learn a subset of the known restrictions on the filler-gap dependency from English sentences in the L2 corpus that L2ers can potentially encounter in their English learning. Examining LSTM LMs' behaviors on controlled sentences designed with the filler-gap dependency, we show the characteristics of L2ers' sentence processing using the information-theoretic metric of surprisal that quantifies violations of the filler-gap dependency or wh-licensing interaction effects. Furthermore, comparing L2ers' LMs with native speakers' LM in light of processing the filler-gap dependency, we not only note that in their sentence processing both L2ers' LM and native speakers' LM can track abstract syntactic structures involved in the filler-gap dependency, but also show using linear mixed-effects regression models that there exist significant differences between them in processing such a dependency.