scholarly journals Deep Learning based NLP Techniques In Text to Speech Synthesis for Communication Recognition

2020 ◽  
Vol 2 (4) ◽  
pp. 209-215
Author(s):  
Eriss Eisa Babikir Adam
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Speech Synthesis ◽  
Large Scale ◽  
Feature Representations ◽  
Large Scale Data ◽  
Learning Techniques ◽  
Text To Speech Synthesis

The computer system is developing the model for speech synthesis of various aspects for natural language processing. The speech synthesis explores by articulatory, formant and concatenate synthesis. These techniques lead more aperiodic distortion and give exponentially increasing error rate during process of the system. Recently, advances on speech synthesis are tremendously moves towards deep learning process in order to achieve better performance. Due to leverage of large scale data gives effective feature representations to speech synthesis. The main objective of this research article is that implements deep learning techniques into speech synthesis and compares the performance in terms of aperiodic distortion with prior model of algorithms in natural language processing.

scholarly journals A Natural Language Processing Tool for Large-Scale Data Extraction from Echocardiography Reports

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153749 ◽  
Author(s):  
Chinmoy Nath ◽  
Mazen S. Albaghdadi ◽  
Siddhartha R. Jonnalagadda
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Data Extraction ◽  
Large Scale Data ◽  
Natural Language Processing Tool ◽  
Scale Data

scholarly journals Application of Natural Language Processing on Character Computing : A Short Review

2021 ◽  
Author(s):  
KOUSHIK DEB
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Personality Trait ◽  
Prediction Models ◽  
Short Review ◽  
Learning Techniques ◽  
Trait Prediction

Character Computing consists of not only personality trait recognition, but also correlation among these traits. Tons of research has been conducted in this area. Various factors like demographics, sentiment, gender, LIWC, and others have been taken into account in order to understand human personality. In this paper, we have concentrated on the factors that could be obtained from available data using Natural Language Processing. It has been observed that the most successful personality trait prediction models are highly dependent on NLP techniques. Researchers across the globe have used different kinds of machine learning and deep learning techniques to automate this process. Different combinations of factors lead the research in different directions. We have presented a comparative study among those experiments and tried to derive a direction for future development.

Deep Learning Approaches for Textual Sentiment Analysis

2020 ◽  
pp. 171-182 ◽  
Author(s):  
Tamanna Sharma ◽  
Anu Bajaj ◽  
Om Prakash Sangwan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Computational Technique ◽  
Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

Measuring happiness of large-scaled online Turkish unstructured data (Preprint)

2020 ◽  
Author(s):  
Esra Kahya Özyirmidokuz ◽  
Kumru Uyar ◽  
Raian Ali ◽  
Eduard Alexandru Stoica ◽  
Betül Karakaş
Keyword(s):  
Social Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Well Being ◽  
Emotional Awareness ◽  
Large Scale Data ◽  
Processing Algorithms ◽  
Scale Data

BACKGROUND Measuring online Turkish happiness requires a Turkish happiness dictionary which could reflect norms and social values more culturally and linguistically instead of using a translation-oriented method. Analyzing data without neglecting cultural characteristics will not be reliable. Turkish translation of an English word in the Affective Norms of English Words (ANEW) dictionary does not express the same feeling of a Turkish word. In addition, existing emotional dictionaries are not developed for specifically for the social networks with emoticons. OBJECTIVE This research presents the Turkish Happiness Index (THI) which is a set of psychological normative happiness scores to measure an average level of happiness of Turkish online unstructured large-scale data. A well-being informatics analytics research is also done by using THI. METHODS Turkish Happiness Index was completely generated on social networks. 20000 words were extracted with web text mining from social networks. Natural Language Processing algorithms were applied. After data reduction quantitative research methodology is applied. The happiness scores were based detected based on 667 participants’ subjective happiness levels and their thoughts about the 1874 Turkish words. Alexithymia scale was also used to identify the emotional awareness of the participants. The evaluations of the words were done in the dimension of valence using the Self-Assessment Manikin in an online platform. NLP was used to measure online Turkish happiness of data. Data was collected from Facebook with negative #war and positive #family hashtags in a duration of one month using a 3rd party software tool. Natural language processing algorithms including tokenization, transformation, filtering and stemming after converting data to documents. The happiness levels of the documents based on hashtags were determined using the Turkish Happiness Index dictionary. RESULTS THI which contains 345 words and their happiness scores in the Turkish language was developed. The THI is given in Appendix 1. We also put a comparison between words of dictionaries to understand the cultural differences. CONCLUSIONS THI provide researchers with standard materials through which they can automatically measure online happiness of Turkish large-scale data. THI can be used in in real-time big data analytics.

scholarly journals A Deep Insight in Challenges of Natural Language Processing and Usage of Deep Learning

2020 ◽  
Vol 8 (12s3) ◽  
pp. 50-54
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automatic Speech Recognition ◽  
Data Driven ◽  
Deep Insight ◽  
Pos Tagging ◽  
Learning Techniques ◽  
Using Data

Natural Language Processing (NLP) using the power of artificial intelligence has empowered the understanding of the language used by human. It has also enhanced the effectiveness of the communication between human and computers. The complexity and diversity of the huge datasets have raised the requirement for automatic analysis of the linguistic data by using data-driven approaches. The performance of the data-driven approaches is improved after the usage of different deep learning techniques in various application areas of NLP like Automatic Speech Recognition, POS tagging etc. The paper addresses the challenges faced in NLP and the use of deep learning techniques in different application areas of NLP.

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