Normalization of Transliterated Mongolian Words Using Seq2Seq Model with Limited Data

The huge increase in social media use in recent years has resulted in new forms of social interaction, changing our daily lives. Due to increasing contact between people from different cultures as a result of globalization, there has also been an increase in the use of the Latin alphabet, and as a result a large amount of transliterated text is being used on social media. In this study, we propose a variety of character level sequence-to-sequence (seq2seq) models for normalizing noisy, transliterated text written in Latin script into Mongolian Cyrillic script, for scenarios in which there is a limited amount of training data available. We applied performance enhancement methods, which included various beam search strategies, N-gram-based context adoption, edit distance-based correction and dictionary-based checking, in novel ways to two basic seq2seq models. We experimentally evaluated these two basic models as well as fourteen enhanced seq2seq models, and compared their noisy text normalization performance with that of a transliteration model and a conventional statistical machine translation (SMT) model. The proposed seq2seq models improved the robustness of the basic seq2seq models for normalizing out-of-vocabulary (OOV) words, and most of our models achieved higher normalization performance than the conventional method. When using test data during our text normalization experiment, our proposed method which included checking each hypothesis during the inference period achieved the lowest word error rate (WER = 13.41%), which was 4.51% fewer errors than when using the conventional SMT method.

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Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme

Sensors ◽

10.3390/s21227582 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7582

Author(s):

Venkatachalam Kandasamy ◽

Pavel Trojovský ◽

Fadi Al Machot ◽

Kyandoghere Kyamakya ◽

Nebojsa Bacanin ◽

...

Keyword(s):

Social Media ◽

Learning Algorithm ◽

Comprehensive Evaluation ◽

Training Data ◽

Support Vector ◽

Stacked Autoencoder ◽

Learning Scheme ◽

Ensemble Machine Learning ◽

Learning Techniques ◽

N Gram

The current population worldwide extensively uses social media to share thoughts, societal issues, and personal concerns. Social media can be viewed as an intelligent platform that can be augmented with a capability to analyze and predict various issues such as business needs, environmental needs, election trends (polls), governmental needs, etc. This has motivated us to initiate a comprehensive search of the COVID-19 pandemic-related views and opinions amongst the population on Twitter. The basic training data have been collected from Twitter posts. On this basis, we have developed research involving ensemble deep learning techniques to reach a better prediction of the future evolutions of views in Twitter when compared to previous works that do the same. First, feature extraction is performed through an N-gram stacked autoencoder supervised learning algorithm. The extracted features are then involved in a classification and prediction involving an ensemble fusion scheme of selected ma-chine learning techniques such as decision tree (DT), support vector machine (SVM), random forest (RF) , and K-nearest neighbour (KNN). all individual results are combined/fused for a better prediction by using both mean and mode techniques. Our proposed scheme of an N-gram stacked encoder integrated in an ensemble machine learning scheme outperforms all the other existing competing techniques such unigram autoencoder, bigram autoencoder, etc. Our experimental results have been obtained from a comprehensive evaluation involving a dataset extracted from open-source data available from Twitter that were filtered by using the keywords “covid”, “covid19”, “coronavirus”, “covid-19”, “sarscov2”, and “covid_19”.

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An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection

Journal of Data and Information Quality ◽

10.1145/3418036 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1-25

Author(s):

Anurag Roy ◽

Shalmoli Ghosh ◽

Kripabandhu Ghosh ◽

Saptarshi Ghosh

Keyword(s):

Large Fraction ◽

Training Data ◽

Writing Style ◽

Robust Algorithms ◽

Normalization Methods ◽

Textual Data ◽

Digitized Documents ◽

Noisy Text ◽

Text Normalization

A large fraction of textual data available today contains various types of “noise,” such as OCR noise in digitized documents, noise due to informal writing style of users on microblogging sites, and so on. To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i.e., for cleaning different kinds of noise in the text. There have been several efforts towards cleaning or normalizing noisy text; however, many of the existing text normalization methods are supervised and require language-dependent resources or large amounts of training data that is difficult to obtain. We propose an unsupervised algorithm for text normalization that does not need any training data/human intervention. The proposed algorithm is applicable to text over different languages and can handle both machine-generated and human-generated noise. Experiments over several standard datasets show that text normalization through the proposed algorithm enables better retrieval and stance detection, as compared to that using several baseline text normalization methods.

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“I can’t just post anything I want”: Self-management of South Korean sports stars on social media

International Review for the Sociology of Sport ◽

10.1177/10126902211014122 ◽

2021 ◽

pp. 101269022110141

Author(s):

Eunhye Yoo

Keyword(s):

Social Media ◽

Self Management ◽

Public And Private ◽

Daily Lives ◽

The Public ◽

South Korean ◽

Digital Labor ◽

Close Relationship ◽

Study Participants ◽

Depth Interviews

This study explores the influence and sociocultural meaning of self-management of South Korean sports stars in the context of their social media activity. The study utilizes netnography to analyze social media posts to determine the meaning of sports stars’ self-management. Data were collected through in-depth interviews with study participants. Ten South Korean sports stars, who are active users of Instagram, were selected as the study participants. Photographs, videos, and stories from their accounts—around 1800 posts in total—were analyzed. The results indicated that the sports stars attempted to share their daily lives on social media to build a close relationship with the public. Moreover, they used their accounts to publicize their commercialized selves and to promote their sponsors. They uploaded only strictly composed and curated posts on their accounts as a form of self-censorship. Finally, it was determined that digital labor was used for self-management on social media, where there is no distinction between public and private territory. A sports star has become a self-living commercial today, and self-management is now a prerequisite for survival. Thus, self-management on social media has become a requirement for sports stars.

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A sentiment analysis system for social media using machine learning techniques: Social enablement

Digital Scholarship in the Humanities ◽

10.1093/llc/fqy037 ◽

2018 ◽

Vol 34 (3) ◽

pp. 569-581 ◽

Cited By ~ 1

Author(s):

Sujata Rani ◽

Parteek Kumar

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Media Analysis ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Analysis Tool ◽

Data Set ◽

Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

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Linguistically Annotated Reordering: Evaluation and Analysis

Computational Linguistics ◽

10.1162/coli_a_00009 ◽

2010 ◽

Vol 36 (3) ◽

pp. 535-568 ◽

Cited By ~ 2

Author(s):

Deyi Xiong ◽

Min Zhang ◽

Aiti Aw ◽

Haizhou Li

Keyword(s):

Large Scale ◽

Statistical Machine Translation ◽

Training Data ◽

Linguistic Knowledge ◽

Analysis Method ◽

Complementary Information ◽

New Approach ◽

Parse Trees ◽

New Challenges ◽

Insight Into

Linguistic knowledge plays an important role in phrase movement in statistical machine translation. To efficiently incorporate linguistic knowledge into phrase reordering, we propose a new approach: Linguistically Annotated Reordering (LAR). In LAR, we build hard hierarchical skeletons and inject soft linguistic knowledge from source parse trees to nodes of hard skeletons during translation. The experimental results on large-scale training data show that LAR is comparable to boundary word-based reordering (BWR) (Xiong, Liu, and Lin 2006), which is a very competitive lexicalized reordering approach. When combined with BWR, LAR provides complementary information for phrase reordering, which collectively improves the BLEU score significantly. To further understand the contribution of linguistic knowledge in LAR to phrase reordering, we introduce a syntax-based analysis method to automatically detect constituent movement in both reference and system translations, and summarize syntactic reordering patterns that are captured by reordering models. With the proposed analysis method, we conduct a comparative analysis that not only provides the insight into how linguistic knowledge affects phrase movement but also reveals new challenges in phrase reordering.

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Extraction of Named Entities from Social Media Text in Tamil Language Using N-Gram Embedding for Disaster Management

Nature-Inspired Computation in Data Mining and Machine Learning - Studies in Computational Intelligence ◽

10.1007/978-3-030-28553-1_10 ◽

2019 ◽

pp. 207-223

Author(s):

G. Remmiya Devi ◽

M. Anand Kumar ◽

K. P. Soman

Keyword(s):

Social Media ◽

Disaster Management ◽

Named Entities ◽

Social Media Text ◽

N Gram

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Analisis Sentimen Data Twitter Tentang Pasangan Capres-Cawapres Pemilu 2019 Dengan Metode Lexicon Based Dan Support Vector Machine

Jurnal Ilmiah FIFO ◽

10.22441/fifo.2019.v11i2.004 ◽

2019 ◽

Vol 11 (2) ◽

pp. 144

Author(s):

Danar Wido Seno ◽

Arief Wibowo

Keyword(s):

Social Media ◽

Support Vector Machine ◽

Sentiment Analysis ◽

Vice President ◽

Training Data ◽

Support Vector ◽

New Words ◽

Textual Data ◽

Data Content ◽

Combination Of Methods

Social media writing content growing make a lot of new words that appear on Twitter in the form of words and abbreviations that appear so that sentiment analysis is increasingly difficult to get high accuracy of textual data on Twitter social media. In this study, the authors conducted research on sentiment analysis of the pairs of candidates for President and Vice President of Indonesia in the 2019 Elections. To obtain higher accuracy results and accommodate the problem of textual data development on Twitter, the authors conducted a combination of methods to conduct the sentiment analysis with unsupervised and supervised methods. namely Lexicon Based. This study used Twitter data in October 2018 using the search keywords with the names of each pair of candidates for President and Vice President of the 2019 Elections totaling 800 datasets. From the study with 800 datasets the best accuracy was obtained with a value of 92.5% with 80% training data composition and 20% testing data with a Precision value in each class between 85.7% - 97.2% and Recall value for each class among 78, 2% - 93.5%. With the Lexicon Based method as a labeling dataset, the process of labeling the Support Vector Machine dataset is no longer done manually but is processed by the Lexicon Based method and the dictionary on the lexicon can be added along with the development of data content on Twitter social media.

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Optimizing Tokenization Choice for Machine Translation across Multiple Target Languages

Prague Bulletin of Mathematical Linguistics ◽

10.1515/pralin-2017-0025 ◽

2017 ◽

Vol 108 (1) ◽

pp. 257-269 ◽

Cited By ~ 4

Author(s):

Nasser Zalmout ◽

Nizar Habash

Keyword(s):

Machine Translation ◽

Performance Enhancement ◽

Statistical Machine Translation ◽

Target Language ◽

Source Language ◽

Context Variable ◽

Significant Performance ◽

Morphologically Rich Languages ◽

Target Languages ◽

Language Text

AbstractTokenization is very helpful for Statistical Machine Translation (SMT), especially when translating from morphologically rich languages. Typically, a single tokenization scheme is applied to the entire source-language text and regardless of the target language. In this paper, we evaluate the hypothesis that SMT performance may benefit from different tokenization schemes for different words within the same text, and also for different target languages. We apply this approach to Arabic as a source language, with five target languages of varying morphological complexity: English, French, Spanish, Russian and Chinese. Our results show that different target languages indeed require different source-language schemes; and a context-variable tokenization scheme can outperform a context-constant scheme with a statistically significant performance enhancement of about 1.4 BLEU points.

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