Annotating patient clinical records with syntactic chunks and named entities: the Harvey corpus

The free text notes typed by physicians during patient consultations contain valuable information for the study of disease and treatment. These notes are difficult to process by existing natural language analysis tools since they are highly telegraphic (omitting many words), and contain many spelling mistakes, inconsistencies in punctuation, and non-standard word order. To support information extraction and classification tasks over such text, we describe a de-identified corpus of free text notes, a shallow syntactic and named entity annotation scheme for this kind of text, and an approach to training domain specialists with no linguistic background to annotate the text. Finally, we present a statistical chunking system for such clinical text with a stable learning rate and good accuracy, indicating that the manual annotation is consistent and that the annotation scheme is tractable for machine learning

A Survey of Biological Entity Recognition Approaches

There has been growing interest in the task of Named Entity Recognition (NER) and a lot of research has been done in this direction in last two decades. Particularly, a lot of progress has been made in the biomedical domain with emphasis on identifying domain-specific entities and often the task being known as Biological Named Entity Recognition (BER). The task of biological entity recognition (BER) has been proved to be a challenging task due to several reasons as identified by many researchers. The recognition of biological entities in text and the extraction of relationships between them have paved the way for doing more complex text-mining tasks and building further applications. This paper looks at the challenges perceived by the researchers in BER task and investigates the works done in the domain of BER by using the multiple approaches available for the task

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

Learning for clinical named entity recognition without manual annotations

Background: Named entity recognition (NER) systems are commonly built using supervised methods that use machine learning to learn from corpora manually annotated with named entities. However, manually annotating corpora is very expensive and laborious. Materials and methods: In this paper, a novel method is presented for training clinical NER systems that does not require any manual annotations. It only requires a raw text corpus and a resource like UMLS that can give a list of named entities along with their semantic types. Using these two resources, annotations are automatically obtained to train machine learning methods. The method was evaluated on the NER shared-task datasets of i2b2 2010 and SemEval 2014. Results: On the SemEval 2014 dataset for recognizing diseases and disorders, the method obtained F-measure of 0.693 for exact matching and of 0.773 allowing overlaps. This is comparable to many supervised systems in the past that had used manual annotations for training. On the i2b2 2010 dataset for recognizing problems, tests and treatments, the method obtained F-measures of 0.451, 0.338 and 0.204 respectively for exact matching and of 0.721, 0.587 and 0.475 respectively allowing overlaps. These results are better than an existing unsupervised method. Conclusions: Experiments on standard datasets showed that the new method performed well. The method is general and could be applied to recognize entities of other types on other genres of text without needing manual annotations