Automatic Annotation of Parameters from Nanodevice Development Research Papers

Dieb, Thaer M.; Yoshioka, Masaharu; Hara, Shinjiro; Newton, Marcus C.

doi:10.3115/v1/w14-4810

Cited by 3 publications

(6 citation statements)

References 9 publications

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“…The extraction of experimental information is supposed to be applied as a preprocessing step for such shared data construction in nanoinformatics. Our preliminary work [36], [37], [38] is recognized as one of the main efforts in applying natural language processing to extract such information [39].…”

Section: Discussionmentioning

confidence: 99%

NaDev: An Annotated Corpus to Support Information Extraction from Research Papers on Nanocrystal Devices

Dieb¹,

Yoshioka²,

Hara

2016

Journal of Information Processing

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The process of nanocrystal device development is not well systematized. To support this process, analysis of the information produced by developmental experiments is required. In this study, we constructed an annotated corpus to support the extraction of experimental information from relevant publications. We designed the corpus-construction guidelines by cooperating with a domain expert. We evaluated these guidelines through corpusconstruction experiments with graduate students from this domain, and then evaluated the corpus with the domain expert. In the corpus construction experiments, we achieved a sufficient level of Inter-Annotator Agreement by using a loose agreement measure that ignored the term-boundary mismatch problem, and made an agreement corpus that excluded annotations based on misunderstanding the guidelines. The domain expert evaluated this agreement corpus and modified the guidelines based on real examples. Using these guidelines, we finalized the corpus called "NaDev" (Nanocrystal Device development corpus). The NaDev corpus and its construction guidelines will be released via our website, http://nanoinfo.ist.hokudai.ac.jp/. The NaDev corpus aims to support automatic information extraction from publications relevant to nanocrystal device development. This information can be used to solve problems in the nanotechnology domain using the massive availability of fresh information. To the best of our knowledge, this is the first corpus constructed for the development of nanocrystal devices.

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Section: Discussionmentioning

confidence: 99%

NaDev: An Annotated Corpus to Support Information Extraction from Research Papers on Nanocrystal Devices

Dieb¹,

Yoshioka²,

Hara

2016

Journal of Information Processing

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“…Dieb et al generated a tool to automatically collect literature relevant to nanodevice design and a tool to automatically annotate literature on nanodevices [30–31]. A training set, which consisted of two fully annotated papers with 129 sentences, was manually annotated by graduate students with the assistance of an annotation support tool, XConc Suite [32].…”

Section: Reviewmentioning

confidence: 99%

“…With these four tag groups, cascading style annotation could be applied [31]. To automate the annotation process, a biomedical entity extraction method using the supervised machine learning algorithm, support vector machines (SVM), was applied to their literature library.…”

Section: Reviewmentioning

confidence: 99%

“…The tool was further improved by applying a physical quantities list (based on the one listed on the website chemistry.about.com) to refine the extraction of two tags: evaluation parameter and experiment parameter [31]. However, their annotated library only expanded from two to five papers, and the group only used two papers to test their improved system.…”

Section: Reviewmentioning

confidence: 99%

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Using natural language processing techniques to inform research on nanotechnology

Lewinski¹,

McInnes²

2015

Beilstein J. Nanotechnol.

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SummaryLiterature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics.

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“…We previously constructed an annotated corpus called “NaDev” ( Na nocrystal Dev ice Development corpus) [ 27 – 28 ] for research papers on nanocrystal device development. We also proposed a framework to extract information from research papers by using machine learning tools [ 29 – 30 ]. However, this system was only evaluated using the corpus constructed in our preliminary experiment, which was not sufficient to compare automatic information extraction results with those from human annotators.…”

Section: Introductionmentioning

confidence: 99%

Framework for automatic information extraction from research papers on nanocrystal devices

Dieb¹,

Yoshioka²,

Hara³

et al. 2015

Beilstein J. Nanotechnol.

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SummaryTo support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called “ NaDev” (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called “NaDevEx” (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39–73%); however, precision is better (75–97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for characterization papers. Based on these results, we discuss future research plans for improving the performance of the system.

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Automatic Annotation of Parameters from Nanodevice Development Research Papers

Cited by 3 publications

References 9 publications

NaDev: An Annotated Corpus to Support Information Extraction from Research Papers on Nanocrystal Devices

NaDev: An Annotated Corpus to Support Information Extraction from Research Papers on Nanocrystal Devices

Using natural language processing techniques to inform research on nanotechnology

Framework for automatic information extraction from research papers on nanocrystal devices

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