최근 인공지능(Artificial Intelligence; A.I.)의 기술 발전과 함께 이에 대한 관심이 증가하고 있으며 관련 시장도 비약적으로 확대되고 있다. 아직은 초기단계이지만 2000년 이후 현재까지 계속 확장되고 있는 인공지능 기술 분야의 연구방향과 투자 분야에 대한 불확실성을 줄이는 것이 중요한 시점이다. 이러한 기술 변화와 시대적 요구에 따라서 본 연구는 빅데이터(Big Data) 분석방법 중 텍스트 마이닝(Text Mining)과 토픽모델링(Topic Modeling)을 활용하여 기술동향을 살펴보고, 핵심기술과 성장 가능성이 있는 연구의 향후 방향성을 제시하였다. 본 연구의 결과로부터 인공지능의 기술동향에 대한 이해를 바탕으로 향후 연구 방향에 대한 새로운 시사점을 도출할 수 있으리라 기대한다.

Latent Dirichlet Allocation (LDA) 모델 기반의 인공지능(A.I.) 기술 관련 연구 활동 및 동향 분석

A recent “third wave” of neural network (NN) approaches now delivers state-of-the-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper, we survey the current landscape of Neural IR research, paying special attention to the use of learned distributed representations of textual units. We highlight the successes of neural IR thus far, catalog obstacles to its wider adoption, and suggest potentially promising directions for future research.

/pdf/neural-information-retrieval-at-the-end-of-the-early-years-144j0hldyz.pdf

Neural information retrieval: at the end of the early years

The Gene Ontology Consortium (GOC) is a community-based bioinformatics project that classifies gene product function through the use of structured controlled vocabularies. A fundamental application of the Gene Ontology (GO) is in the creation of gene product annotations, evidence-based associations between GO definitions and experimental or sequence-based analysis. Currently, the GOC disseminates 126 million annotations covering >374 000 species including all the kingdoms of life. This number includes two classes of GO annotations: those created manually by experienced biocurators reviewing the literature or by examination of biological data (1.1 million annotations covering 2226 species) and those generated computationally via automated methods. As manual annotations are often used to propagate functional predictions between related proteins within and between genomes, it is critical to provide accurate consistent manual annotations. Toward this goal, we present here the conventions defined by the GOC for the creation of manual annotation. This guide represents the best practices for manual annotation as established by the GOC project over the past 12 years. We hope this guide will encourage research communities to annotate gene products of their interest to enhance the corpus of GO annotations available to all.

/pdf/a-guide-to-best-practices-for-gene-ontology-go-manual-heen3gvhst.pdf

A guide to best practices for Gene Ontology (GO) manual annotation

Scientific publications are documentary representations of defeasible arguments, supported by data and repeatable methods. They are the essential mediating artifacts in the ecosystem of scientific communications. The institutional “goal” of science is publishing results. The linear document publication format, dating from 1665, has survived transition to the Web. Intractable publication volumes; the difficulty of verifying evidence; and observed problems in evidence and citation chains suggest a need for a web-friendly and machine-tractable model of scientific publications. This model should support: digital summarization, evidence examination, challenge, verification and remix, and incremental adoption. Such a model must be capable of expressing a broad spectrum of representational complexity, ranging from minimal to maximal forms. The micropublications semantic model of scientific argument and evidence provides these features. Micropublications support natural language statements; data; methods and materials specifications; discussion and commentary; challenge and disagreement; as well as allowing many kinds of statement formalization. The minimal form of a micropublication is a statement with its attribution. The maximal form is a statement with its complete supporting argument, consisting of all relevant evidence, interpretations, discussion and challenges brought forward in support of or opposition to it. Micropublications may be formalized and serialized in multiple ways, including in RDF. They may be added to publications as stand-off metadata. An OWL 2 vocabulary for micropublications is available at http://purl.org/mp
 . A discussion of this vocabulary along with RDF examples from the case studies, appears as OWL Vocabulary and RDF Examples in Additional file1. Micropublications, because they model evidence and allow qualified, nuanced assertions, can play essential roles in the scientific communications ecosystem in places where simpler, formalized and purely statement-based models, such as the nanopublications model, will not be sufficient. At the same time they will add significant value to, and are intentionally compatible with, statement-based formalizations. We suggest that micropublications, generated by useful software tools supporting such activities as writing, editing, reviewing, and discussion, will be of great value in improving the quality and tractability of biomedical communications.

/pdf/micropublications-a-semantic-model-for-claims-evidence-16bnwlzdmn.pdf

Micropublications: a semantic model for claims, evidence, arguments and annotations in biomedical communications

Fully automated text mining (TM) systems promote efficient literature searching, retrieval, and review but are not sufficient to produce ready-to-consume curated documents. These systems are not meant to replace biocurators, but instead to assist them in one or more literature curation steps. To do so, the user interface is an important aspect that needs to be considered for tool adoption. The BioCreative Interactive task (IAT) is a track designed for exploring user-system interactions, promoting development of useful TM tools, and providing a communication channel between the biocuration and the TM communities. In BioCreative V, the IAT track followed a format similar to previous interactive tracks, where the utility and usability of TM tools, as well as the generation of use cases, have been the focal points. The proposed curation tasks are user-centric and formally evaluated by biocurators. In BioCreative V IAT, seven TM systems and 43 biocurators participated. Two levels of user participation were offered to broaden curator involvement and obtain more feedback on usability aspects. The full level participation involved training on the system, curation of a set of documents with and without TM assistance, tracking of time-on-task, and completion of a user survey. The partial level participation was designed to focus on usability aspects of the interface and not the performance per se. In this case, biocurators navigated the system by performing pre-designed tasks and then were asked whether they were able to achieve the task and the level of difficulty in completing the task. In this manuscript, we describe the development of the interactive task, from planning to execution and discuss major findings for the systems tested.

/pdf/overview-of-the-interactive-task-in-biocreative-v-1qk0t2htb9.pdf

Overview of the interactive task in BioCreative V

BioCreative: Critical Assessment of Information Extraction in Biology is an international community-wide effort for evaluating text mining and information extraction systems applied to the biological domain (http://www.biocreative.org/). The Challenge Evaluations and the accompanying BioCreative Workshops bring together the text mining and biology communities to drive the development of text mining systems that can be integrated into the biocuration workflow and the knowledge discovery process. To address the current barriers in using text mining in biology, BioCreative has further been conducting user requirement analysis, user-based evaluations and fostering standard development for text mining tool re-use and integration. This DATABASE virtual issue captures the major results from the BioCreative-2012 Workshop on Interactive Text Mining in the Biocuration Workflow and is the fifth special issue devoted to BioCreative.

BioCreative-2012 virtual issue.

BioC is a new simple XML format for sharing biomedical text and annotations and libraries to read and write that format. This promotes the development of interoperable tools for natural language processing (NLP) of biomedical text. The interoperability track at the BioCreative IV workshop featured contributions using or highlighting the BioC format. These contributions included additional implementations of BioC, many new corpora in the format, biomedical NLP tools consuming and producing the format and online services using the format. The ease of use, broad support and rapidly growing number of tools demonstrate the need for and value of the BioC format. Database URL: http://bioc.sourceforge.net/.

https://www.zora.uzh.ch/id/eprint/104530/1/bau053.pdf

BioC interoperability track overview.

A Feature Generation Algorithm with Applications to Bio- logical Sequence Classiﬁcation

BioC is a simple XML data format for text, annotations, and relations. PubAnnotation is a repository of text annotations focused on the life science literature. A conversion tool between BioC XML and the JSON import / export format of PubAnnotation has been developed, BioCconvert. As a demonstration, the Ab3P gold standard abbreviation annotations are being made available through PubAnnotation. Keywords—BioC, PubAnnotation, interoperability, biomedical annotations

/pdf/biocconvert-a-conversion-tool-between-bioc-and-pubannotation-1yal3nklwb.pdf

BioCconvert: A Conversion Tool Between BioC and PubAnnotation.

The main approach of traditional information retrieval (IR) is to examine how many words from a query appear in a document. A drawback of this approach, however, is that it may fail to detect relevant documents where no or only few words from a query are found. The semantic analysis methods such as LSA (latent semantic analysis) and LDA (latent Dirichlet allocation) have been proposed to address the issue, but their performance is not superior compared to common IR approaches. Here we present a query-document similarity measure motivated by the Word Mover's Distance. Unlike other similarity measures, the proposed method relies on neural word embeddings to calculate the distance between words. Our method is efficient and straightforward to implement. The experimental results on TREC and PubMed show that our approach provides significantly better performance than BM25. We also discuss the pros and cons of our approach and show that there is a synergy effect when the word embedding measure is combined with the BM25 function.

Bridging the Gap: a Semantic Similarity Measure between Queries and Documents

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