Named Entity Recognition (NER) is a task in Information Extraction consisting in identifying and classifying just some types of information elements, called Named Entities (NE). I have tried to collect and curate some publications form Arxiv that related to NER, and the results were listed here. Please enjoy it!
Sentiment analysis is the area which deals with judgments, responses as well as feelings, which is generated from texts, being extensively used in fields like data mining, web mining, and social media analytics because sentiments are the most essential characteristics to judge the human behavior. I have tried to collect and curate some publications form Arxiv that related to the sentiment analysis in social media, and the results were listed here. Please enjoy it!
Machine Translation ( MT) is the task of automatically converting one natural language to another, preserving the meaning of the input text, and producing fluent text in the output language. I have tried to collect and curate some publications form Arxiv that related to the multi-lingual machine translation for low resource language, and the results were listed here. Please enjoy it!
Visual Question Answering (VQA) is a recent topic in computer vision and natural language processing that has attracted a great deal of attention from deep learning, computer vision and natural language processing communities.. I have tried to collect and curate some publications form Arxiv that related to the visual question answering, and the results were listed here. Please enjoy it!
Sentiment analysis is the area which deals with judgments, responses as well as feelings, which is generated from texts, being extensively used in fields like data mining, web mining, and social media analytics because sentiments are the most essential characteristics to judge the human behavior. I have tried to collect and curate some publications form Arxiv that related to the sentiment analysis using neural networks, and the results were listed here. Please enjoy it!
Question Answering (QA) Systems is an automated approach to retrieve correct responses to the questions asked by human in natural language. I have tried to collect and curate some publications form Arxiv that related to question answering dataset, and the results were listed here. Please enjoy it!
Multi-document summarization is an automatic process to create a concise and comprehensive document, called summary from multiple documents. I have tried to collect and curate some publications form Arxiv that related to multi document summarization, and the results were listed here. Please enjoy it!
Aspect-based sentiment analysis deals with capturing sentiments expressed towards each aspect of entities. I have tried to collect and curate some publications form Arxiv that related to the aspect-based sentiment analysis, and the results were listed here. Please enjoy it!
Machine Translation ( MT) is the task of automatically converting one natural language to another, preserving the meaning of the input text, and producing fluent text in the output language. I have tried to collect and curate some publications form Arxiv that related to the machine translation for low resource language, and the results were listed here. Please enjoy it!
The main objective of extractive summarization can be concisely formulated as extracting text inputs containing information on the most important concepts described in the input text or texts. I have tried to collect and curate some publications form Arxiv that related to the extractive summarization, and the results were listed here. Please enjoy it!
Abstractive summary is a technique in which the summary is created by either rephrasing or using the new words, rather than simply extracting the relevant phrases. I have tried to collect and curate some publications form Arxiv that related to the abstractive summarization, and the results were listed here. Please enjoy it!
Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your training data. I have tried to collect and curate some publications form Arxiv that related to the generative adversarial networks, and the results were listed here. Please enjoy it!
Recurrent neural networks (RNNs) are a class of neural networks that are naturally suited to processing time-series data and other sequential data. I have tried to collect and curate some publications form Arxiv that related to the recurrent neural networks, and the results were listed here. Please enjoy it!
Computational biology, a biology branch involving the use of computers and computer science to understand and model the structures and processes of life. It involves the use of computational methods (e.g. algorithms) for the representation and simulation of biological systems, as well as for the interpretation of experimental data, often on a very large scale. I have tried to collect and curate some publications that related to the computational biology and bioinformatics using neural networks and the results were listed here. Please enjoy it!
A convolutional neural network (CNN) is most popular deep learning algorithm used for image related applications. I have tried to collect and curate some publications form Arxiv that related to the Convolutional Neural Networks (CNNs), and the results were listed here. Please enjoy it!
Deep learning is a set of machine learning algorithms that attempt to model high-level abstractions in input data using multiple non-linear transformations. Some of the major advances in deep learning have been made in speech recognition, computer vision, and natural language processing. Considering that data volume is growing exponentially, deep learning is becoming increasingly important in the predictive analysis of big data. I have tried to collect and curate some publications form bioRxiv that related to the deep learning application in biology, and the results were listed here. Please enjoy it!
News articles are usually written by journalists based on statements taken from interviews with public figures. Attribution from such statements provides important information and it can be extracted from news articles to build a knowledge base by developing a sequential tagging scheme such as entity recognition. This research applies two deep learning architectures: recurrent neural networks-based and transformer-based, to establish public figures statement attribution and extraction models in the Indonesian Language. The experiments are conducted using five deep-learning model architectures with two different corpus sizes to investigate the impact of corpus size on each model’s performance. The experiments show that the best model for the RNN-based architecture is PFSA-ID-BLWCA which achieves 81.34 % F1 score, and the best model for the transformer-based is PFSA-ID-TWCA which obtains 81.01 % F1 score. This research also discovers that the size of the corpus influences the model performances. Furthermore, the study lays a foundation to overcome the attribution extraction in another language, especially low-resource languages, with some necessary adjustments.
By far, the corpus for the quotation extraction and quotation attribution tasks in Indonesian is still limited in quantity and depth. This study aims to develop an Indonesian corpus of public figure statements attributions and a baseline model for attribution extraction, so it will contribute to fostering research in information extraction for the Indonesian language.
Extracting information from unstructured data becomes a challenging task for computational linguistics. Public figure’s statement attributed by journalists in a story is one type of information that can be processed into structured data. Therefore, having the knowledge base about this data will be very beneficial for further use, such as for opinion mining, claim detection and fact-checking. This study aims to understand statement extraction tasks and the models that have already been applied to formulate a framework for further study.
Text is one of the media used by humans to communicate and interact every day, especially in the field of education, for example, in writing a final project report. The most common thing in writing text is typographical errors. Based on these problems, an application is needed to help the writer to be able to identify typographical errors in the Indonesian Language document. The application developed using Laravel version 5.8 for web application and Python version 3 for processing datasets, developing model, and developing web services. Model built uses the NLTK library and Jaro-Winkler distance algorithm implemented using the pylibjaro library. The dataset uses an open-source dataset in the form of a list of words from KBBI. This application only supports pdf files. The results of the model are applied to the web services with output in the form of JSON data. The JSON data contains a list of words that have true or false values, the number of document words, the number of correct words, the number of incorrect words, and the time of program execution.
I have tried to collect and curate some Python-based Github repository linked to the machine translation task, and the results were listed here. Please enjoy it to support your research about developing machine translation model using Python!
An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. This RNN type introduced by Hochreiter and Schmidhuber. I have tried to collect and curate some Python-based Github repository linked to the LSTM, and the results were listed here. Please enjoy it to support your research about LSTM using Python!
Sentiment Analysis ( SA) is a field of study that analyzes people’s feelings or opinions from reviews or opinions. Sentiment analysis can be seen as a natural language processing task, the task is to develop a system that understands people’s language. I have tried to collect and curate some Python-based Github repository linked to the sentiment analysis task, and the results were listed here. Please enjoy it to support your research about sentiment analysis using Python!
Object detection is part of the computer vision tasks related to identify or detect an object from an image or video. I have tried to collect and curate some Python-based Github repository linked to the object detection task, and the results were listed here. Please enjoy it to support your research about object detection using Python!
Information Extraction refers to the automatic extraction of structured information such as entities, relationships between entities, and attributes that describe entities from unstructured sources. I have tried to collect and curate some publications form bioRxiv that related to the information extraction in biology, such as relation extraction and feature extraction. The results were listed here. Please enjoy it!
Semi-supervised learning is a learning paradigm concerned with the study of how computers and natural systems, such as humans, learn in the presence of both labeled and unmarked data. I have tried to collect and curate some publications form Arxiv that related to the semi-supervised learning, and the results were listed here. Please enjoy it!
Computational biology, a biology branch involving the use of computers and computer science to understand and model the structures and processes of life. It involves the use of computational methods (e.g. algorithms) for the representation and simulation of biological systems, as well as for the interpretation of experimental data, often on a very large scale. I have tried to collect and curate some publications that related to the computational biology and bioinformatics using neural networks and the results were listed here. Please enjoy it!
A convolutional neural network (CNN) is most popular deep learning algorithm used for image related applications. I have tried to collect and curate some publications form Arxiv that related to the Convolutional Neural Networks (CNNs), and the results were listed here. Please enjoy it!
I will use this site to inform you about my academic journeys, includes teaching, research, publication, and other academic activities like fellowship.
As academia, having a website to publish portfolios is really important. GitHub Pages will be a perfect solution because it doesn’t cost anything. There is no need to buy a domain and web hosting service. Everything is all in Github. This tutorial will guide you to build your GitHub Pages. Please enjoy it!
Information Extraction (IE) is used to extract useful information from unstructured or semi-structured data. I have tried to collect and curate some publications form bioRxiv that related to the information extraction in biology, and the results were listed here. Please enjoy it!
Visual Question Answering (VQA) is a recent topic in computer vision and natural language processing that has attracted a great deal of attention from deep learning, computer vision and natural language processing communities.. I have tried to collect and curate some publications form Arxiv that related to the visual question answering, and the results were listed here. Please enjoy it!