Spanish named entity recognition in the biomedical domain

Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer ReviewedPostprint (author's final draft

An annotated corpus with nanomedicine and pharmacokinetic parameters

A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration’s Drugs@FDA online database. In this work, we present the development of the Engineered Nanomedicine Database corpus to support the evaluation of nanomedicine entity extraction. The data were manually annotated for 21 entity mentions consisting of nanomedicine physicochemical characterization, exposure, and biologic response information of 41 Food and Drug Administration-approved nanomedicines. We evaluate the reliability of the manual annotations and demonstrate the use of the corpus by evaluating two state-of-the-art named entity extraction systems, OpenNLP and Stanford NER. The annotated corpus is available open source and, based on these results, guidelines and suggestions for future development of additional nanomedicine corpora are provided

Disease Name Extraction from Clinical Text Using Conditional Random Fields

The aim of the research done in this thesis was to extract disease and disorder names from clinical texts. We utilized Conditional Random Fields (CRF) as the main method to label diseases and disorders in clinical sentences. We used some other tools such as MetaMap and Stanford Core NLP tool to extract some crucial features. MetaMap tool was used to identify names of diseases/disorders that are already in UMLS Metathesaurus. Some other important features such as lemmatized versions of words, and POS tags were extracted using the Stanford Core NLP tool. Some more features were extracted directly from UMLS Metathesaurus, including semantic types of words. We participated in the SemEval 2014 competition\u27s Task 7 and used its provided data to train and evaluate our system. Training data contained 199 clinical texts, development data contained 99 clinical texts, and the test data contained 133 clinical texts, these included discharge summaries, echocardiogram, radiology, and ECG reports. We obtained competitive results on the disease/disorder name extraction task. We found through ablation study that while all features contributed, MetaMap matches, POS tags, and previous and next words were the most effective features