Automatic Action Extraction for Short Text Conversation Using Unsupervised Learning

<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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Unsupervised learning of object identities and their parts in a hierarchical visual memory

Frontiers in Computational Neuroscience ◽

10.3389/conf.neuro.10.2009.14.168 ◽

1970 ◽

Author(s):

Jenia Jitsev ◽

Christoph von der Malsburg

Keyword(s):

Unsupervised Learning ◽

Visual Memory

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Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.13 ◽

2017 ◽

Vol 7 (8) ◽

pp. 30

Author(s):

Hyeuk Kim

Keyword(s):

Machine Learning ◽

Principal Component Analysis ◽

Cluster Analysis ◽

Unsupervised Learning ◽

Principal Component ◽

Component Analysis ◽

Baseball Players ◽

Partitioning Around Medoids ◽

Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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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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An Unsupervised Learning Algorithm to Compute Fluid Volumes From NMR T1-T2 Logs in Unconventional Reservoirs

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description ◽

10.30632/pjv59n5-2018a4 ◽

2018 ◽

Vol 59 ◽

pp. 617-632 ◽

Cited By ~ 3

Author(s):

Lalitha Venkataramanan ◽

◽

Noyan Evirgen ◽

David F. Allen ◽

Albina Mutina ◽

...

Keyword(s):

Unsupervised Learning ◽

Learning Algorithm ◽

Unconventional Reservoirs

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Klasifikasi pada Tempat Tinggal Menurut Provinsi dan Jenis Kepemilikan Berdasarkan Algoritma K-Means

STRING (Satuan Tulisan Riset dan Inovasi Teknologi) ◽

10.30998/string.v4i3.5932 ◽

2020 ◽

Vol 4 (3) ◽

pp. 247

Author(s):

Dwi Swasono Rachmad

Keyword(s):

Data Mining ◽

Unsupervised Learning ◽

Residential Buildings ◽

Government Agency ◽

Role Of Government ◽

The Republic ◽

Household Processing ◽

Central Statistics

Housing is derived from the word house which means a place that has a place to live which will stay or stop in a certain time. Housing is a residence that has been grouped into a place that has facilities and infrastructure. The problem in this study focuses on the type of residential ownership in the form of SHM ART, SHM Non ART, NON SHM and others. These four types can be used to know the percentage of ownership in all provinces in Indonesia. Due to the fact that there is still a lot of information about the type of certificate ownership, there is still not much ownership. Therefore, the use of the k-Means algorithm as a data mining concept in the form of clusters, where the data already has parameters or values that fall into the category of unsupervised learning. That data produced the best. The data was obtained from published sources of the Republic of Indonesia government agency, namely the Central Statistics Agency data with the category of household processing with self-owned residential buildings purchased from developers or non-developers by province and type of ownership in 2016 throughout Indonesia. In conducting the dataset, researchers used the RapidMiner application as a clustering process application. This research shows that there are more types of ownership in the SHM ART, but for other values it is still smaller than the value in other types of ownership which is the second largest value. So, in this case, the role of government in providing assistance in the process of ownership in order to become SHM ART is very important.

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