Representations of Time Expressions for Temporal Relation
            Extraction with Convolutional Neural Networks

Lin, Chen; Miller, Timothy A.; Dligach, Dmitriy; Bethard, Steven; Savova, Guergana

doi:10.18653/v1/w17-2341

Cited by 32 publications

(13 citation statements)

References 17 publications

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“…Tourille and colleagues explored neural networks and domain adaptation strategies (37). Chen and colleagues (38) approach. Some latest trends include DL models that combine a small portion of labeled data with unlabeled publicly available data [Google News (30) and social media] to achieve results about 0.02 F1 below IAA (40).…”

Section: Latest Cnlp Application Developmentsmentioning

confidence: 99%

“…The prerequisite for this trend to continue is access to shareable data resources as also pointed out in the 2016 survey article. The colon and brain cancer THYME corpus was used in several general domain conference and workshop articles (37,38,40,(67)(68)(69), whereas a radiology report dataset from a 2007 challenge (available from ref. 70) was used in another (71), and SEER-provided (although unshared thus not available for distribution) corpus was used in yet another (72).…”

Section: Shareable Resources For Nlp In Oncologymentioning

confidence: 99%

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Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

et al. 2019

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Current models for correlating electronic medical records with-omics data largely ignore clinical text, which is an important source of phenotype information for patients with cancer. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment. Insights from this real-world data will catalyze clinical care, research, and regulatory activities. Natural language processing (NLP) methods are needed to extract these rich cancer phenotypes from clinical text. Here, we review the advances of NLP and information extraction methods relevant to oncology based on publications from PubMed as well as NLP and machine learning conference proceedings in the last 3 years. Given the interdisciplinary nature of the fields of oncology and information extraction, this analysis serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.

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Section: Latest Cnlp Application Developmentsmentioning

confidence: 99%

Section: Shareable Resources For Nlp In Oncologymentioning

confidence: 99%

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

et al. 2019

Self Cite

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“…Traditional machine learning and deep learning methods are now enabling exciting technical advances in this NLP domain; however, more work is needed before complete temporal data can be reliably extracted from clinical free text. [93][94][95][96][97] Taken together, the aforementioned clinical NLP tasks have the potential to provide comprehensive cancercare summaries at the individual and population level, which we hope will improve quality, efficiency, and bias in both patient care and research.…”

Section: Applications Of Nlp In Oncologymentioning

confidence: 99%

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Bitterman

Miller

Mak

et al. 2021

International Journal of Radiation Oncology*Biology*Physics

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“…In the last couple of years, the work in biomedical NLP was dominated by applications of deep learning to: punctuation restoration [68], text classification [69], relation extraction [70] [71] [72] [73], information retrieval [74], and similarity judgments [75], among other exciting progress in biomedical language processing. For a more detailed exploration of recent topics, the BioNLP Annual Workshop [76] covers the most researched and debatable areas.…”

Section: Future Workmentioning

confidence: 99%

Biomedical Term Extraction: NLP Techniques in Computational Medicine

Sandoval¹,

Díaz²,

Llanos³

et al. 2019

IJIMAI

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Artificial Intelligence (AI) and its branch Natural Language Processing (NLP) in particular are main contributors to recent advances in classifying documentation and extracting information from assorted fields, Medicine being one that has gathered a lot of attention due to the amount of information generated in public professional journals and other means of communication within the medical profession. The typical information extraction task from technical texts is performed via an automatic term recognition extractor. Automatic Term Recognition (ATR) from technical texts is applied for the identification of key concepts for information retrieval and, secondarily, for machine translation. Term recognition depends on the subject domain and the lexical patterns of a given language, in our case, Spanish, Arabic and Japanese. In this article, we present the methods and techniques for creating a biomedical corpus of validated terms, with several tools for optimal exploitation of the information therewith contained in said corpus. This paper also shows how these techniques and tools have been used in a prototype.

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Representations of Time Expressions for Temporal Relation Extraction with Convolutional Neural Networks

Cited by 32 publications

References 17 publications

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records

Clinical Natural Language Processing for Radiation Oncology: A Review and Practical Primer

Biomedical Term Extraction: NLP Techniques in Computational Medicine

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