Sigit Purnomo

Sigit Purnomo

Senior Lecturer at Informatics Department, Universitas Atma Jaya Yogyakarta

Research about Information Extraction in Biology Published in bioRxiv

2 minute read

Information Extraction refers to the automatic extraction of structured information such as entities, relationships between entities, and attributes that describe entities from unstructured sources. (Sarawagi, 2008). 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!

Last updated: May 29, 2020
Source : bioRxiv

No. Year Title URL
1 2020 Deep feature extraction of single-cell transcriptomes by generative adversarial network View
2 2020 New alignment-based sequence extraction software (ALiBaSeq) and its utility for deep level phylogenetics View
3 2019 FastFeatGen: Faster parallel feature extraction from genome sequences and efficient prediction of DNA N6-methyladenine sites View
4 2019 Sequoia: An interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets View
5 2019 Feature extraction approach in single-cell gene expression profiling for cell-type marker identification View
6 2019 Relation extraction between bacteria and biotopes from biomedical texts with attention mechanisms and domain-specific contextual View
7 2019 Bridging the TB data gap: in silico extraction of rifampicin-resistant tuberculosis diagnostic test results from whole genome View
8 2019 Using distant supervision to augment manually annotated data for relation extraction View
9 2019 A methodology for morphological feature extraction and unsupervised cell classification View
10 2019 Multiomics data analysis using tensor decomposition based unsupervised feature extraction –Comparison with DIABLO– View
11 2018 scCapsNet: a deep learning classifier with the capability of interpretable feature extraction, applicable for single cell RNA data View
12 2018 EXtrACtor, a tool for multiple queries and data extractions from the EXAC and gnomAD database View
13 2018 Bazam : A rapid method for read extraction and realignment of high throughput sequencing data View
14 2018 Plant leaf tooth feature extraction View
15 2018 SeqTailor: a user-friendly webserver for the extraction of DNA or protein sequences from next-generation sequencing data View
16 2018 rawMSA: Proper Deep Learning makes protein sequence profiles and feature extraction obsolete View
17 2018 Tensor decomposition–based unsupervised feature extraction for integrated analysis of TCGA data on microRNA expression and promoter View
18 2018 CardiacProfileR: An R package for extraction and visualisation of heart rate profiles from wearable fitness trackers View
19 2018 Principal component analysis based unsupervised feature extraction applied to single cell gene expression analysis View
20 2018 Autometa: Automated extraction of microbial genomes from individual shotgun metagenomes View
21 2018 MaREA: Metabolic feature extraction, enrichment and visualization of RNAseq data View
22 2017 Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data View
23 2017 ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data View
24 2016 Ontology-based workflow extraction from texts using word sense disambiguation View
25 2016 Automatic time-series phenotyping using massive feature extraction View
26 2016 De novo extraction of microbial strains from metagenomes reveals intra-species niche partitioning View

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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.

PFSA-ID: An Annotated Indonesian Corpus and Baseline Model of Public Figures Statements Attributions

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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.

Understanding quotation extraction and attribution: towards automatic extraction of public figure’s statements for journalism in Indonesia

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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.

Research about Named Entity Recognition Published in ArXiv

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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!