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Current information extraction efforts in the biomedical domain tend to focus on finding entities and facts in structured databases or MEDLINE abstracts. We apply a gene and protein name tagger …...

We present Pro3Gres, a deep-syntactic, fast dependency parser that combines a hand- written competence grammar with proba- bilistic performance disambiguation and that has been used in the biomedical domain. We …...

Several methods for automatically gen- erating labeled examples that can be used as training data for WSD systems have been proposed, including a semi- supervised approach based on relevance feedback …...

We propose a non-parametric Bayesian model for unsupervised semantic parsing. Follow- ing Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the …...

Measuring semantic relatedness between words or concepts is a crucial process to many Natural Language Processing tasks. Exiting methods exploit semantic evidence from a single knowledge source, and are predominantly …...

Scientific publications contain many references to method terminologies used during scientific experiments. New terms are constantly created within the research community, especially in the biomedical domain where thousands of papers …...

Event extraction is a popular research topic in natural language processing. Several event extrac- tion tasks have been defined for both the newswire and biomedical domains. In general, different systems …...

AbstractWe consider the problem of disambiguating concept mentions appearing in documents and grounding them in multiple knowledge bases, where each knowledge base addresses some aspects of the domain. This problem …...

AbstractNamed Entity Recognition (NER) is an important component of natural language processing (NLP), with applicability in biomedical domain, enabling knowledge-discovery from medical texts. Due to the fact that for the …...

AbstractThe task of relation classification in the biomedical domain is complex due to the presence of samples obtained from heterogeneous sources such as research articles, discharge summaries, or electronic health …...

AbstractIn this report we summarize the results of the 2017 AMR SemEval shared task. The task consisted of two separate yet related subtasks. In the parsing subtask, participants were asked …...

AbstractNamed entity recognition (NER) identifies typed entity mentions in raw text. While the task is well-established, there is no universally used tagset: often, datasets are annotated for use in downstream …...

AbstractThere has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text …...

AbstractOut-of-vocabulary (oov) words cause serious troubles in solving natural language tasks with a neural network. Existing approaches to this problem resort to using subwords, which are shorter and more ambiguous …...

AbstractTransformer encoder models exhibit strong performance in single-domain applications. However, in a cross-domain situation, using a sub-word vocabulary model results in sub-word overlap. This is an issue when there is …...

AbstractThe paradigm of pre-training followed by finetuning has become a standard procedure for NLP tasks, with a known problem of domain shift between the pre-training and downstream corpus. Previous works …...