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1
Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns ...
Abstract: Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as, causality between disease and symptoms, between medications and side effects, between genes and diseases, etc. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by ... : 22 pages without comment ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.2203.06592
https://arxiv.org/abs/2203.06592
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2
Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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3
Redditors in Recovery: Text Mining Reddit to Investigate Transitions into Drug Addiction
In: ArXiv (2018)
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4
Query Segmentation For E-Commerce Sites
Gong, Xiaojing. - 2013
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5
A-Coord input: augmented pen-based interactions by combining auxiliary input channels
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6
Harmonic modification and data adaptive filtering based approach to robust pitch estimation
In: International journal of speech technology. - Boston, Mass. [u.a.] : Kluwer Acad. Publ. 14 (2011) 4, 339-349
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OLC Linguistik
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7
Robust speech dereverberation based on blind adaptive estimation of acoustic channels
In: Institute of Electrical and Electronics Engineers. IEEE transactions on audio, speech and language processing. - New York, NY : Inst. 19 (2011) 4, 775-787
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OLC Linguistik
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