Deep Structured Clustering of Short Text

<div>The article presents the results of the study of the communicative competence of the politicians on the basis of the analysis of their messages on their official pages of the Facebook social network. The research used the following general scientific methods: descriptive and comparative, as well as analysis, synthesis and generalization. The quantitative content analysis method with qualitative elements was used to distinguish the peculiarities of information messages that provide communication of the deputies of Verkhovna Rada (Ukrainian Parliament) on their official Facebook pages. Information messages have been analyzed by the following three criteria: subject matter, structure and language.</div> For the first time the article draws a parallel between communicative competence and the ability to communicate with voters on the official pages of Facebook which is the most popular social network in Ukraine. As it is established, communicative competence in the analyzed cases is caused not by education, but by previous professional activity of a politician. The most successful and high-quality communication was from the current parliamentarian who worked as a journalist in the past. More than half of the messages that provided successful communication consisted of sufficiently structured short text and a video. The topic covers the activity of the parliamentarian in the Verkhovna Rada and in his district. More than half of the messages are spoken in the first person.The findings of the study can be used in teaching such subjects as Political PR and Electronic PR, and may be of interest to politicians and their assistants.Key words: competence and competency, communicative competence, political discourse, official page of the deputy of Verkhovna Rada of Ukraine on the Facebook social network, subject matter and structure of the information message, first-person narrative, correspondence of communication to the level of communicative competence.

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Solving the Message Classification Problem in Voice Interaction Systems

Vestnik MEI ◽

10.24160/1993-6982-2020-5-132-139 ◽

2020 ◽

Vol 5 (5) ◽

pp. 132-139

Author(s):

Ivan E. Kurilenko ◽

◽

Igor E. Nikonov ◽

Keyword(s):

Artificial Intelligence ◽

Classification Problem ◽

Subject Area ◽

Text Messages ◽

Case Based Reasoning ◽

Short Text ◽

Proposed Modification ◽

Voice Interaction ◽

Text Content ◽

Case Based

A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.

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A Study on Bestseller Short Text Semantics Analysis Using Topic Model

The Journal of Image and Cultural Contents ◽

10.24174/jicc.2018.10.15.101 ◽

2018 ◽

Vol 15 ◽

pp. 101-112

Author(s):

So-Hyun Park ◽

Ae-Rin Song ◽

Young-Ho Park ◽

Sun-Young Ihm

Keyword(s):

Topic Model ◽

Short Text

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Short Text Sentiment Classification Based on Context Reconstruction

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2012.00055 ◽

2012 ◽

Vol 38 (1) ◽

pp. 55-67 ◽

Cited By ~ 6

Author(s):

Zhen YANG ◽

Ying-Xu LAI ◽

Li-Juan DUAN ◽

Yu-Jian LI

Keyword(s):

Sentiment Classification ◽

Short Text

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Attention-based Joint Representation Learning Network for Short text Classification

Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence ◽

10.1145/3404555.3404578 ◽

2020 ◽

Author(s):

Xinyue Liu ◽

Yexuan Tang

Keyword(s):

Text Classification ◽

Representation Learning ◽

Short Text ◽

Learning Network ◽

Joint Representation

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Glottalization and linking in the L2 speech of Czech learners of Spanish, Italian and Portuguese

Second language Research ◽

10.1177/02676583211015803 ◽

2021 ◽

pp. 026765832110158

Author(s):

Radek Skarnitzl ◽

Petr Čermák ◽

Pavel Šturm ◽

Zora Obstová ◽

Jan Hricsina

Keyword(s):

Logistic Regression Model ◽

Native Speakers ◽

Mixed Effects ◽

Lexical Stress ◽

The Other ◽

Target Language ◽

Romance Languages ◽

Short Text ◽

Sound Pattern ◽

Frequent Use

The use of linking or glottalization contributes to the characteristic sound pattern of a language, and the use of one in place of the other may affect a speaker’s comprehensibility and fluency in certain contexts. In this study, native speakers of Czech, a language that is associated with a frequent use of glottalization in vowel-initial word onsets, are examined in the second language (L2) context of three Romance languages that predominantly employ linking between words (Spanish, Italian and Portuguese). In total, 29 native speakers and 51 non-native learners were asked to read a short text in the respective language. The learners were divided into two groups based on their experience with the target language. A number of other factors were examined in a mixed-effects logistic regression model (segmental context, lexical stress, prosodic breaks, and the semantic status of the words). The main results show that, regardless of the target language, the more experienced (ME) learners displayed significantly lower rates of glottalization than the less experienced (LE) learners, but significantly higher rates than native speakers. The pedagogical implications of the results are discussed.

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