Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jun;2018(Short Paper):371-377.

Syntactic Patterns Improve Information Extraction for Medical Search

Roma Patel  1   2 , Yinfei Yang  1   2   3   4 , Iain Marshall  3 , Ani Nenkova  1 , Byron C Wallace  4
Affiliations

Syntactic Patterns Improve Information Extraction for Medical Search

Roma Patel et al. Proc Conf. 2018 Jun.

Abstract

Medical professionals search the published literature by specifying the type of patients, the medical intervention(s) and the outcome measure(s) of interest. In this paper we demonstrate how features encoding syntactic patterns improve the performance of state-of-the-art sequence tagging models (both linear and neural) for information extraction of these medically relevant categories. We present an analysis of the type of patterns exploited, and the semantic space induced for these, i.e., the distributed representations learned for identified multi-token patterns. We show that these learned representations differ substantially from those of the constituent unigrams, suggesting that the patterns capture contextual information that is otherwise lost.

PubMed Disclaimer

Figures

Figure 1:
Figure 1:
Scatter of PCA-reduced embeddings clustered using K-means. <> brackets show the syntactic pattern n-grams given by Autoslog-TS that are embedding in the same space as unigrams.
See this image and copyright information in PMC

References

    1. Alamri Abdulaziz and Stevenson Mark. 2016. A corpus of potentially contradictory research claims from cardiovascular research abstracts. Journal of biomedical semantics 7(1):36. - PMC - PubMed
    1. Boudin Florian, Nie Jian-Yun, and Dawes Martin. 2010a. Clinical information retrieval using document and PICO structure. In Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, June 2–4, 2010, Los Angeles, California, USA pages 822–830.
    1. Boudin Florian, Shi Lixin, and Nie Jian-Yun. 2010b. Improving medical information retrieval with PICO element detection. In Advances in Information Retrieval, 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28–31, 2010 Proceedings. pages 50–61.
    1. Carlson Andrew, Betteridge Justin, Kisiel Bryan, Settles Burr, Hruschka Estevam R. Jr., and Mitchell Tom M.. 2010. Toward an architecture for never-ending language learning. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2010, Atlanta, Georgia, USA, July 11–15, 2010.
    1. Demner-Fushman Dina and Lin Jimmy. 2007. Answering clinical questions with knowledge-based and statistical techniques. Computational Linguistics 33(1):63–103.

LinkOut - more resources