ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2015
Automatic Extraction and Post-coordination of Spatial Relations in Consumer Language.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Understanding spatial language in radiology: Representation framework, annotation, and spatial relation extraction from chest X-ray reports using deep learning.Journal of biomedical informatics · 2020Article
- Rad-SpatialNet: A Frame-based Resource for Fine-Grained Spatial Relations in Radiology Reports.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2020Article
- Spatial Relation Extraction from Radiology Reports using Syntax-Aware Word Representations.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Clinical MetaData ontology: a simple classification scheme for data elements of clinical data based on semantics.BMC medical informatics and decision making · 2019Article
- CUILESS2016: a clinical corpus applying compositional normalization of text mentions.Journal of biomedical semantics · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To incorporate ontological concepts in natural language processing (NLP) it is often necessary to combine simple concepts into complex concepts (post-coordination). This is especially true in consumer language, where a more limited vocabulary forces consumers to utilize highly productive language that is almost impossible to pre-coordinate in an ontology. Our work focuses on recognizing an important case for post-coordination in natural language: spatial relations between disorders and anatomical structures. Consumers typically utilize such spatial relations when describing symptoms. We describe an annotated corpus of 2,000 sentences with 1,300 spatial relations, and a second corpus of 500 of these relations manually normalized to UMLS concepts. We use machine learning techniques to recognize these relations, obtaining good performance. Further, we experiment with methods to normalize the relations to an existing ontology. This two-step process is analogous to the combination of concept recognition and normalization, and achieves comparable results.
Indexed as
Identifiers
26958247PMC4765706What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.