• Genomics & Informatics
• /
• v.18 no.2
• /
• pp.13.1-13.6
• /
• 2020

The prototype version of the full-text corpus of Genomics & Informatics has recently been archived in a GitHub repository. The full-text publications of volumes 10 through 17 are also directly downloadable from PubMed Central (PMC) as XML files. During the Biomedical Linked Annotation Hackathon 6 (BLAH6), we experimented with converting, annotating, and updating 301 PMC full-text articles of Genomics & Informatics using PubAnnotation, a system that provides a convenient way to add PMC publications based on PMCID. Thus, this review aims to provide a tutorial overview of practicing the iterative task of named entity recognition with the PubAnnotation/PubDictionaries/TextAE ecosystem. We also describe developing a conversion tool between the Genia tagger output and the JSON format of PubAnnotation during the hackathon.

• Genomics & Informatics
• /
• v.18 no.2
• /
• pp.18.1-18.5
• /
• 2020

Finding publications that propose alternative methods to animal experiments is an important but time-consuming task since researchers need to perform various queries to literature databases and screen many articles to assess two important aspects: the relevance of the article to the research question, and whether the article's proposed approach qualifies to being an alternative method. We are currently developing a Web application to support finding alternative methods to animal experiments. The current (under development) version of the application utilizes external tools and resources for document processing, and relies on the PubAnnotation ecosystem for annotation querying, annotation storage, dictionary-based tagging of cell lines, and annotation visualization. Currently, our two PubAnnotation repositories for discourse elements contain annotations for more than 110k PubMed documents. Further, we created an annotator for cell lines that contain more than 196k terms from Cellosaurus. Finally, we are experimenting with TextAE for annotation visualization and for user feedback.

• Genomics & Informatics
• /
• v.19 no.3
• /
• pp.23.1-23.7
• /
• 2021

Currently, coronavirus disease 2019 (COVID-19) literature has been increasing dramatically, and the increased text amount make it possible to perform large scale text mining and knowledge discovery. Therefore, curation of these texts becomes a crucial issue for Bio-medical Natural Language Processing (BioNLP) community, so as to retrieve the important information about the mechanism of COVID-19. PubAnnotation is an aligned annotation system which provides an efficient platform for biological curators to upload their annotations or merge other external annotations. Inspired by the integration among multiple useful COVID-19 annotations, we merged three annotations resources to LitCovid data set, and constructed a cross-annotated corpus, LitCovid-AGAC. This corpus consists of 12 labels including Mutation, Species, Gene, Disease from PubTator, GO, CHEBI from OGER, Var, MPA, CPA, NegReg, PosReg, Reg from AGAC, upon 50,018 COVID-19 abstracts in LitCovid. Contain sufficient abundant information being possible to unveil the hidden knowledge in the pathological mechanism of COVID-19.

• 한국어정보학회:학술대회논문집
• /
• 2016.10a
• /
• pp.85-89
• /
• 2016

프레임넷 (FrameNet) 프로젝트는 버클리에서 1997년에 처음 제안했으며, 최근에는 다양한 언어적 특징을 반영하여 여러 국가에서 사용되고 있다. 하지만 문장의 프레임을 분석하는 것은 자연언어처리 전문가들이 많은 시간을 들여야 한다. 이 때문에, 한국어 프레임넷을 처음 만들 때는 충분한 훈련을 받은 번역가들이 영어 프레임넷의 문장들과 그 주석 정보들을 직접 번역하는 방법을 사용했다. 결과적으로 상대적으로 적은 비용이 들지만, 여전히 한 문장에 여러 번 등장하는 프레임 정보를 모두 번역하고 에러를 분석해야 했기에 많은 노력이 들어갔다. 본 연구에서는 일본어와 한국어의 언어적 유사성을 사용하여 비교적 적은 비용으로 한국어 프레임넷을 확장하는 방법을 제시한다. 또한 프레임넷에 친숙하지 않은 사용자가 더욱 쉽게 프레임 정보를 활용할 수 있도록 PubAnnotation 기술을 도입하고 "조사"라는 특성을 고려한 Valence pattern 분류를 통해 한국어 공개 프레임넷 사이트를 개선하였다.

• Annual Conference on Human and Language Technology
• /
• 2016.10a
• /
• pp.85-89
• /
• 2016

프레임넷 (FrameNet) 프로젝트는 버클리에서 1997년에 처음 제안했으며, 최근에는 다양한 언어적 특징을 반영하여 여러 국가에서 사용되고 있다. 하지만 문장의 프레임을 분석하는 것은 자연언어처리 전문가들이 많은 시간을 들여야 한다. 이 때문에, 한국어 프레임넷을 처음 만들 때는 충분한 훈련을 받은 번역가들이 영어 프레임넷의 문장들과 그 주석 정보들을 직접 번역하는 방법을 사용했다. 결과적으로 상대적으로 적은 비용이 들지만, 여전히 한 문장에 여러 번 등장하는 프레임 정보를 모두 번역하고 에러를 분석해야 했기에 많은 노력이 들어갔다. 본 연구에서는 일본어와 한국어의 언어적 유사성을 사용하여 비교적 적은 비용으로 한국어 프레임넷을 확장하는 방법을 제시한다. 또한 프레임넷에 친숙하지 않은 사용자가 더욱 쉽게 프레임 정보를 활용할 수 있도록 PubAnnotation 기술을 도입하고 "조사"라는 특성을 고려한 Valence pattern 분류를 통해 한국어 공개 프레임넷 사이트를 개선하였다.

• Genomics & Informatics
• /
• v.17 no.2
• /
• pp.17.1-17.3
• /
• 2019

Text mining has become an important research method in biology, with its original purpose to extract biological entities, such as genes, proteins and phenotypic traits, to extend knowledge from scientific papers. However, few thorough studies on text mining and application development, for plant molecular biology data, have been performed, especially for rice, resulting in a lack of datasets available to solve named-entity recognition tasks for this species. Since there are rare benchmarks available for rice, we faced various difficulties in exploiting advanced machine learning methods for accurate analysis of the rice literature. To evaluate several approaches to automatically extract information from gene/protein entities, we built a new dataset for rice as a benchmark. This dataset is composed of a set of titles and abstracts, extracted from scientific papers focusing on the rice species, and is downloaded from PubMed. During the 5th Biomedical Linked Annotation Hackathon, a portion of the dataset was uploaded to PubAnnotation for sharing. Our ultimate goal is to offer a shared task of rice gene/protein name recognition through the BioNLP Open Shared Tasks framework using the dataset, to facilitate an open comparison and evaluation of different approaches to the task.

• Genomics & Informatics
• /
• v.18 no.2
• /
• pp.15.1-15.6
• /
• 2020

Named entity recognition tools are used to identify mentions of biomedical entities in free text and are essential components of high-quality information retrieval and extraction systems. Without good entity recognition, methods will mislabel searched text and will miss important information or identify spurious text that will frustrate users. Most tools do not capture non-contiguous entities which are separate spans of text that together refer to an entity, e.g., the entity "type 1 diabetes" in the phrase "type 1 and type 2 diabetes." This type is commonly found in biomedical texts, especially in lists, where multiple biomedical entities are named in shortened form to avoid repeating words. Most text annotation systems, that enable users to view and edit entity annotations, do not support non-contiguous entities. Therefore, experts cannot even visualize non-contiguous entities, let alone annotate them to build valuable datasets for machine learning methods. To combat this problem and as part of the BLAH6 hackathon, we extended the TextAE platform to allow visualization and annotation of non-contiguous entities. This enables users to add new subspans to existing entities by selecting additional text. We integrate this new functionality with TextAE's existing editing functionality to allow easy changes to entity annotation and editing of relation annotations involving non-contiguous entities, with importing and exporting to the PubAnnotation format. Finally, we roughly quantify the problem across the entire accessible biomedical literature to highlight that there are a substantial number of non-contiguous entities that appear in lists that would be missed by most text mining systems.

• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.101-106
• /
• 2005

Recently, as the size of genetic knowledge grows faster, automated analysis and systemization into high-throughput database has become hot issue. One essential task is to recognize and identify genomic entities and discover their relations. However, ambiguity of name entities is a serious problem because of their multiplicity of meanings and types. So far, many effective techniques have been proposed to analyze documents. Yet, accuracy is high when the data fits the model well. The purpose of this paper is to design and implement a document classification system for identifying entity problems using text/data mining combination, supplemented by rich data mining algorithms to enhance its performance. we propose RTP ost system of different style from any traditional method, which takes fault tolerant system approach and data mining strategy. This feedback cycle can enhance the performance of the text mining in terms of accuracy. We experimented our system for classifying RB-related documents on PubMed abstracts to verify the feasibility.

• Genomics & Informatics
• /
• v.19 no.3
• /
• pp.25.1-25.5
• /
• 2021

The coronavirus disease 2019 (COVID-19) pandemic has led to a flood of research papers and the information has been updated with considerable frequency. For society to derive benefits from this research, it is necessary to promote sharing up-to-date knowledge from these papers. However, because most research papers are written in English, it is difficult for people who are not familiar with English medical terms to obtain knowledge from them. To facilitate sharing knowledge from COVID-19 papers written in English for Japanese speakers, we tried to construct a dictionary with an open license by assigning Japanese terms to MeSH unique identifiers (UIDs) annotated to words in the texts of COVID-19 papers. Using this dictionary, 98.99% of all occurrences of MeSH terms in COVID-19 papers were covered. We also created a curated version of the dictionary and uploaded it to Pub-Dictionary for wider use in the PubAnnotation system.