• The KIPS Transactions:PartB
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• v.12B no.3 s.99
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• pp.365-368
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• 2005

We propose a speech act analysis method for Korean dialogue using Support Vector Machines (SVM). We use a lexical form of a word, its part of speech (POS) tags, and bigrams of POS tags as sentence features and the contexts of the previous utterance as context features. We select informative features by Chi square statistics. After training SVM with the selected features, SVM classifiers determine the speech act of each utterance. In experiment, we acquired overall $90.54\%$ of accuracy with dialogue corpus for hotel reservation domain.

• Korean Journal of Cognitive Science
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• v.18 no.2
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• pp.179-191
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• 2007

In a goal-oriented dialogue, spoken' intentions can be represented by domain actions that consist of pairs of a speech art and a concept sequence. The domain action prediction of user's utterance is useful to correct some errors that occur in a speech recognition process, and the domain action prediction of system's utterance is useful to generate flexible responses. In this paper, we propose a model to predict a domain action of the next utterance using a neural network. The proposed model predicts the next domain action by using a dialogue history vector and a current domain action as inputs of the neural network. In the experiment, the proposed model showed the precision of 80.02% in speech act prediction and the precision of 82.09% in concept sequence prediction.

• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.288-293
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• 2011

To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the support vector machine (SVM), and the maximum entropy model (MEM). In experiments with a goal-oriented dialogue corpus in the schedule management domain, we found that the MEM has lighter hardware requirements, whereas the SVM has better performance characteristics.

• Journal of KIISE:Software and Applications
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• v.35 no.9
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• pp.554-561
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• 2008

Prediction technique of user's intention can be used as a post-processing method for reducing the search space of an automatic speech recognizer. Prediction technique of system's intention can be used as a pre-processing method for generating a flexible sentence. To satisfy these practical needs, we propose a statistical model to predict speakers' intentions that are generalized into pairs of a speech act and a concept sequence. Contrary to the previous model using simple n-gram statistic of speech acts, the proposed model represents a dialogue history of a current utterance to a feature set with various linguistic levels (i.e. n-grams of speech act and a concept sequence pairs, clue words, and state information of a domain frame). Then, the proposed model predicts the intention of the next utterance by using the feature set as inputs of CRFs (Conditional Random Fields). In the experiment in a schedule management domain, The proposed model showed the precision of 76.25% on prediction of user's speech act and the precision of 64.21% on prediction of user's concept sequence. The proposed model also showed the precision of 88.11% on prediction of system's speech act and the Precision of 87.19% on prediction of system's concept sequence. In addition, the proposed model showed 29.32% higher average precision than the previous model.

• Annual Conference on Human and Language Technology
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• 2006.10e
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• pp.246-253
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• 2006

대화 에이전트와 관련된 지금까지의 연구는 대개 대상 도메인을 한정하고, 특정 목적을 달성하기 위해 사용자와 대화할 수 있는 에이전트에 관한 연구가 많았다. 본 연구에서는 도메인이 한정되지 않은 일반 도메인 대화에서 화행(speech act)정보를 수동으로 부착시켜 구축한 말뭉치에 대해 소개하고 이 말뭉치를 토대로 자동으로 화행을 분류할 수 있는 유용한 자질들을 선보인다. 그리고 도메인이 한정된 말뭉치와 도메인이 한정되지 않은 말뭉치를 자동으로 화행분류해 본 실험한 결과를 비교하였다.

• Korean Journal of Cognitive Science
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• v.19 no.3
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• pp.297-310
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• 2008

Since speech acts implies speakers' intentions, it is essential to determine speakers' speech acts if we want to implement an intelligent dialogue system. We propose a two-step model for effectively determining speakers' speech acts. In the first step, the proposed model returns speech act candidates by using a neural network model based on machine learning and a predictivity model based on statistics, respectively. In the second step, using speech act candidates which are returned by the predictivity model, the proposed model filters out speech act candidates which are returned by the neural network model. Then, the proposed model selects a speech act with maximum output value among the unremoved speech act candidates. In the experiment on a schedule management domain, the proposed two-step modeling method showed better precisions than the previous methods only using a machine learning model or a probability model.

• Journal of KIISE
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• v.43 no.1
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• pp.80-86
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• 2016

A speech-act is a behavior intended by users in an utterance. Speech-act classification is important in a dialogue system. The machine learning and rule-based methods have mainly been used for speech-act classification. In this paper, we propose a speech-act classification method based on the combination of support vector machine (SVM) and transformation-based learning (TBL). The user's utterance is first classified by SVM that is preferentially applied to categories with a low utterance rate in training data. Next, when an utterance has negative scores throughout the whole of the categories, the utterance is applied to the correction phase by rules. The results from our method were higher performance over the baseline system long with error-reduction.

• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.374-385
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• 2008

Speech act tagging is an important step in various dialogue applications, which recognizes speaker's intentions expressed in natural language utterances. Previous approaches such as rule-based and statistics-based methods utilize the speech acts of previous utterances and sentence features of the current utterance. This paper proposes a method that determines speech acts of the current utterance using the speech acts of the following utterances as well as previous ones. Using the features of following utterances yields the accuracy 95.27%, improving previous methods by 3.65%. Moreover, sentence features of the previous utterances are employed to maximally utilize the information available to the current utterance. By applying the proper probability model for each speech act, final accuracy of 97.97% is achieved.

• The Korean Society of Law and Medicine
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• v.23 no.3
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• pp.117-137
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• 2022

Today, the medical system is changing into a comprehensive health care system in which collaborative relationships between medical professionals and non-medical personnels in neighboring occupational areas. The current medical act brands such "collaboration" as unlicensed medical practice, and punishes non-medical personnel who acted in the risk management of doctors as well as doctors collaborated with non-medical personnel as unlicensed medical practice. In order to narrow the gap between the legal system that regulates unlicensed medical practices and the medical reality, it is necessary to overcome the structural limitations of dualistic, nationalistic, and identity-oriented regulation of unlicensed medical practices. The legal interests of unlicensed medical practice have a dual nature as a personal legal interest of "human life and body" as well as a national legal interest of "maintenance and protection of the nation's medical license system", and it should be noted that the criteria for judging the legal interests protected by the regulations of criminal punishment should be found in "personal legal interest theory." In addition, when determining which behavior is a medical practice and evaluating its risk, the dimension of behavior and measures should be considered in a fair manner without being biased against the subject (identity) of the action. In other words, judging unlicensed medical practice should depend on whether the risk of side effects that may result from the act is reasonably managed. Considering the prospect of therapeutic dialogue between medical professionals and patients, it would be desirable for medical law policies to move in a way that does not fundamentally block the possibility of collaboration among pluralistic medical personalities.

• Ocean and Polar Research
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• v.45 no.2
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• pp.71-87
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• 2023

Climate change causes ocean warming, ocean acidification, sea-level rise, dynamic coastal risk, change of ecosystem structure and function, and degradation of ecosystem services. Not only that, but it has negatively impacted the well-being of people, society, and culture, including food security, water resources, water quality, livelihood, health, welfare, infrastructure, transport, tourism, recreation, and so on, especially by particularly degrading indigenous communities and generating an inequitable distribution of benefits and costs. As pointed out here, these adverse impacts of climate change on the ocean have been emphasized at the international and national levels. In contrast, the ocean field has been neglected in the climate change conversation for too long. However, since the UNFCCC COP 25, the ocean has been drawn into the discussion as a solution to address climate change. Moreover, the U.S. Congress recently unveiled a bill called the 'Ocean-Based Climate Solution Act, OBCSA' that reflects the new paradigm of the international regime. The comprehensive legislative bill includes elements related to climate inequity, a blue economy, and a community-led bottom-up policy mechanism, which will have a significant bearing on the ocean-climate legal system. Therefore, this study reviews the OBCSA and deduces implications with regard to the ocean-climate legal system in Korea.