Identification of Aphasia using Natural Language Processing

Rao, Ananya Ananth; S, Prof. Venkatesh

doi:10.51201/jusst/21/06488

Cited by 8 publications

(10 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lastly, lemmatization is applied in pre-processing stage. Lemmatization is the process of putting together the inflected elements of a word such that they can be identified as a single element, known as the lemma or vocabulary form of the word [30]. After all preprocessing stages are completed, LDA topic modeling is conducted.…”

Section: Methodsmentioning

confidence: 99%

Russia-Ukraine Conflict: A Text Mining Approach through Twitter

Eligüzel

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

The focus of this study is to use social media to investigate the Russia-Ukraine conflict. With the assent of the Russian parliament, Russian President Vladimir Putin proclaimed that they will begin invading Ukraine on February 24, 2022. During the Russia-Ukraine conflict, social media, particularly Twitter, has been heavily used. For that reason, it becomes to strong tool for handling processes during the conflict such as political decision making, organizing humanitarian activities, and proving assistance for victims. As a result, social media becomes the most up-to-date, comprehensive, and large information source for current scenario analysis. A total of 65412 tweets are gathered as a dataset for analysis in the proposed study between February 24 and April 5. Then, for each tweet, a topic modeling method called Latent Dirichlet Allocation (LDA) is used to collect significant topics and their probabilities considering each tweets. Then, using the specified probabilities, Fuzzy c-means is utilized to generate clusters for the entire document. Finally, seven unique clusters have been gathered for processing. N-grams and network analysis are used to examine each resulting cluster for a better understanding. As a result of this study, worldwide public opinion, current situation of civilians, course of the conflict, humanitarian issues during the Russia-Ukraine conflict are extracted.

show abstract

Section: Methodsmentioning

confidence: 99%

Russia-Ukraine Conflict: A Text Mining Approach through Twitter

Eligüzel

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

View full text Add to dashboard Cite

show abstract

“…Logs are a challenging data type for text understanding models because they tend to contain incomplete words, words with special characters (e.g., underscore), and system-specific terms. Consequently, we cleaned the data using standard natural languages processing techniques, such as tokenization [31], lemmatization [16], and removal of English stop words given by the SpaCy library 2 . Given the cleaned logs, we constructed semantic representations of the logs using average Word2Vec 3 vectors as well as SBERT sentence embeddings [27].…”

Section: Datamentioning

confidence: 99%

Log Summarisation for Defect Evolution Analysis

Dolga,

Zmigrod,

Silva

et al. 2023

Proceedings of the 1st International Workshop on Software Defect Datasets

View full text Add to dashboard Cite

Log analysis and monitoring are essential aspects in software maintenance and identifying defects. In particular, the temporal nature and vast size of log data leads to an interesting and important research question: How can logs be summarised and monitored over time? While this has been a fundamental topic of research in the software engineering community, work has typically focused on heuristic-, syntax-, or static-based methods. In this work, we suggest an online semantic-based clustering approach to error logs that dynamically updates the log clusters to enable monitoring code error life-cycles. We also introduce a novel metric to evaluate the performance of temporal log clusters. We test our system and evaluation metric with an industrial dataset and find that our solution outperforms similar systems. We hope that our work encourages further temporal exploration in defect datasets. CCS CONCEPTS• Theory of computation → Unsupervised learning and clustering; • Software and its engineering → Software defect analysis.

show abstract

“…NLP can be divided into two main tasks: natural language understanding (NLU) and natural language generation (NLG). NLU is the task of understanding the meaning of text, while NLG is the task of generating text [12]. NLU is a field of artificial intelligence that enables machines to understand natural language.…”

Section: Figure 1 Chatbot Architecture Frameworkmentioning

confidence: 99%

Virtual Assistant for Thesis Technical Guide Using Artificial Neural Network

Sanjaya,

Bukhori,

Furqon

2023

IJAIDM

View full text Add to dashboard Cite

This study focuses on finding best practice for Artificial Neural Network (ANN) implementation in the information system for student’s thesis technical instructions. The machine learning model applied sequential model, it means ANN only use 1 input layer, a hidden/dense layer and 1 output layer. The Stochastic Gradient Decent (SGD) method was applied into data training process. The results of this study are chatbot applications, and model testing using the confusion matrix. The result of model evaluation are 99,49% accuracy and 91% in F-1 score.

show abstract

Identification of Aphasia using Natural Language Processing

Cited by 8 publications

References 12 publications

Russia-Ukraine Conflict: A Text Mining Approach through Twitter

Russia-Ukraine Conflict: A Text Mining Approach through Twitter

Log Summarisation for Defect Evolution Analysis

Virtual Assistant for Thesis Technical Guide Using Artificial Neural Network

Contact Info

Product

Resources

About