Interacting Knowledge Sources, Inspection and Analysis: Case-studies on Biomedical text processing

Information extraction (IE) is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several activities, such as named entity recognition, event extraction, relationship discovery, and document classification, with the overall goal of translating text into a more structured form. Information on the changes in the effect of a drug, when taken in combination with a second drug, is known as drug–drug interaction (DDI). DDIs can delay, decrease, or enhance absorption of drugs and thus decrease or increase their efficacy or cause adverse effects. Recent research trends have shown several adaptation of recurrent neural networks (RNNs) from text. In this study, we highlight significant challenges of using RNNs in biomedical text processing and propose automatic extraction of DDIs aiming at overcoming some challenges. Our results show that the system is competitive against other systems for the task of extracting DDIs.

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BioC: a minimalist approach to interoperability for biomedical text processing

Database ◽

10.1093/database/bat064 ◽

2013 ◽

Vol 2013 (0) ◽

pp. bat064-bat064 ◽

Cited By ~ 74

Author(s):

D. C. Comeau ◽

R. Islamaj Dogan ◽

P. Ciccarese ◽

K. B. Cohen ◽

M. Krallinger ◽

...

Keyword(s):

Text Processing ◽

Biomedical Text ◽

Minimalist Approach

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A Process Model for Establishing Business Process Crowdsourcing

Australasian Journal of Information Systems ◽

10.3127/ajis.v21i0.1392 ◽

2017 ◽

Vol 21 ◽

Cited By ~ 4

Author(s):

Nguyen Hoang Thuan ◽

Pedro Antunes ◽

David Johnstone

Keyword(s):

Knowledge Base ◽

Case Studies ◽

Business Process ◽

Process Model ◽

Business Processes ◽

Business Strategies ◽

Knowledge Sources ◽

Internet Users ◽

Organisational Strategy

Crowdsourcing can be an organisational strategy to distribute work to Internet users and harness innovation, information, capacities, and variety of business endeavours. As crowdsourcing is different from other business strategies, organisations are often unsure as to how to best structure different crowdsourcing activities and integrate them with other organisational business processes. To manage this problem, we design a process model guiding how to establish business process crowdsourcing. The model consists of seven components covering the main activities of crowdsourcing processes, which are drawn from a knowledge base incorporating diverse knowledge sources in the domain. The built model is evaluated using case studies, suggesting the adequateness and utility of the model.

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Exodus: Materials in Automatic Text Processing. Part I: Morphological, Syntactical and Literary Case Studies. Part II: Concordance

Journal of Biblical Literature ◽

10.2307/3260687 ◽

1985 ◽

Vol 104 (4) ◽

pp. 696

Author(s):

Peter C. Patton ◽

Ferenc Postma ◽

Eep Talstra ◽

Marc Vervenne

Keyword(s):

Case Studies ◽

Text Processing ◽

Automatic Text

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Designing Student-Centered Learning Into a Multimedia Classroom: One Small Step

Volume 2: 11th Biennial Conference on Reliability, Stress Analysis, and Failure Prevention; 7th International Conference on Design Theory and Methodology; JSME Symposium on Design and Production; Mechanical Design Education and History; Computer-Integrated Concurrent Design Conference ◽

10.1115/detc1995-0185 ◽

1995 ◽

Author(s):

Jeffrey S. Heidler ◽

John C. Thompson ◽

Matthew J. Hrinyak ◽

Farrokh Mistree

Keyword(s):

Case Studies ◽

Multimedia Technology ◽

Knowledge Sources ◽

Small Step ◽

Student Centered ◽

Student Centered Learning

Abstract Courses today are instructor-centered in that students rely on the instructor and textbook as knowledge sources and offer themselves as “empty vessels” to be filled with facts and skills. We are interested in exploring student-centered learning using multimedia technology. We want to determine how multimedia can be used to empower students to controltheir own learning. In this paper, we report on a small part of our investigation, namely, the development of a framework for implementing engineering case studies in multimedia to facilitate student-centered learning.

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Dependency parsing of biomedical text with BERT

BMC Bioinformatics ◽

10.1186/s12859-020-03905-8 ◽

2020 ◽

Vol 21 (S23) ◽

Author(s):

Jenna Kanerva ◽

Filip Ginter ◽

Sampo Pyysalo

Keyword(s):

Transfer Learning ◽

Language Processing ◽

State Of The Art ◽

Text Processing ◽

Syntactic Analysis ◽

Biomedical Text ◽

Dependency Parsing ◽

Shared Task ◽

Fine Tune ◽

Selection Of

Abstract Background: Syntactic analysis, or parsing, is a key task in natural language processing and a required component for many text mining approaches. In recent years, Universal Dependencies (UD) has emerged as the leading formalism for dependency parsing. While a number of recent tasks centering on UD have substantially advanced the state of the art in multilingual parsing, there has been only little study of parsing texts from specialized domains such as biomedicine. Methods: We explore the application of state-of-the-art neural dependency parsing methods to biomedical text using the recently introduced CRAFT-SA shared task dataset. The CRAFT-SA task broadly follows the UD representation and recent UD task conventions, allowing us to fine-tune the UD-compatible Turku Neural Parser and UDify neural parsers to the task. We further evaluate the effect of transfer learning using a broad selection of BERT models, including several models pre-trained specifically for biomedical text processing. Results: We find that recently introduced neural parsing technology is capable of generating highly accurate analyses of biomedical text, substantially improving on the best performance reported in the original CRAFT-SA shared task. We also find that initialization using a deep transfer learning model pre-trained on in-domain texts is key to maximizing the performance of the parsing methods.

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