Effective Korean Speech-act Classification Using the Classification Priority Application and a Post-correction Rules

Song, Namhoon; Bae, Kyoungman

doi:10.5626/jok.2016.43.1.80

Cited by 2 publications

(4 citation statements)

References 2 publications

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“…RELATED WORK Automatic speech act classification has been a subject of research for some time, focusing on dialogue acts [12,13,14]. Earlier studies on Korean employed various methods, including Hidden Markov Models [15], maximum entropy models [16], and supervised machine learning algorithms [17]. Unlike Austin and Searle's speech acts, dialogue acts specifically target synchronous language used in direct communication and many of the classification schemes used in research do not align with Austin's or Searle's speech act classification.…”

Section: Introductionmentioning

confidence: 99%

Classifying Speech Acts in Political Communication: A Transformer-based Approach with Weak Supervision and Active Learning

Schmidt,

Niekler,

Kantner

et al. 2023

Annals of Computer Science and Information Systems

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We present a study on the automatic classification of speech acts in the domain of political communication, based on J. R. Searle's classification of illocutionary acts. Our research involves creating a dataset using the US State of the Union corpus and the UN General Debate corpus (UNGD) as data sources.To overcome limited labelled data, we employ a combination of weak supervision and active learning techniques for dataset creation and model training. Through various experiments, we investigate the influence of external and internal factors on speech act classification. In addition, we discuss the potential for further analysis of speech act usage, using the trained model on the UNGD corpus. The findings demonstrate the effectiveness of Transformer-based models for automatic speech act classification, highlight the benefits of weak supervision and active learning for dataset creation and model training, and underscore the potential for large-scale statistical analysis of speech act usage in the domain of political communication.

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Section: Introductionmentioning

confidence: 99%

Classifying Speech Acts in Political Communication: A Transformer-based Approach with Weak Supervision and Active Learning

Schmidt,

Niekler,

Kantner

et al. 2023

Annals of Computer Science and Information Systems

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“…Choi [4] proposed a maximum entropy model (MEM) to determine the speech-act of current utterances using previous utterances as contextual information. Song [5] recommended a support vector machine (SVM) model to preferentially analyze classes with lower distribution when training among a set of classes.…”

Section: Related Workmentioning

confidence: 99%

“…To evaluate the proposed model, we used the reservation † http://news.kbs.co.kr/ tasks corpus that was used in the previous studies [3]- [5], [11] which was transcribed from real conversions occurring when making hotel, airline, and tour reservation. That corpus consists of 528 dialogues, 10,285 utterances, and 17 types of speech-acts.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…However, it is difficult to perfectly interpret a speaker's intention because a speaker indicates his/her intention using various expressions [2]. In order to overcome this problem, most studies adopt supervised machine learning methods using various speech features including lexical, contextual, and other semantic features [3]- [5]. Recently, many deep learning techniques have been applied to natural language processing because they afford a significant advantage with regard to analyzing the semantics of lexical features based on a distributed representation.…”

Section: Introductionmentioning

confidence: 99%

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Speech-Act Classification Using a Convolutional Neural Network Based on POS Tag and Dependency-Relation Bigram Embedding

Yoo

Seo

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose a deep learning based model for classifying speech-acts using a convolutional neural network (CNN). The model uses some bigram features including parts-of-speech (POS) tags and dependency-relation bigrams, which represent syntactic structural information in utterances. Previous classification approaches using CNN have commonly exploited word embeddings using morpheme unigrams. However, the proposed model first extracts two different bigram features that well reflect the syntactic structure of utterances and then represents them as a vector representation using a word embedding technique. As a result, the proposed model using bigram embeddings achieves an accuracy of 89.05%. Furthermore, the accuracy of this model is relatively 2.8% higher than that of competitive models in previous studies.

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Effective Korean Speech-act Classification Using the Classification Priority Application and a Post-correction Rules

Cited by 2 publications

References 2 publications

Classifying Speech Acts in Political Communication: A Transformer-based Approach with Weak Supervision and Active Learning

Classifying Speech Acts in Political Communication: A Transformer-based Approach with Weak Supervision and Active Learning

Speech-Act Classification Using a Convolutional Neural Network Based on POS Tag and Dependency-Relation Bigram Embedding

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