Biomedical Text

We investigate automatic classification of speculative language (‘hedging’), biomedical text using weakly supervisedin machine learning. Our contributions include a precise description of the task with anno- tation guidelines, analysis and …...

Most state-of-the-art wide-coverage parsers are trained on newspaper text and suffer a loss of accuracy in other domains, making parser adaptation a pressing issue. In this paper we demonstrate that …...

Natural language processing modules such as part-of-speech taggers, named-entity recog- nizers and syntactic parsers are commonly evaluated in isolation, under the assumption that artificial evaluation metrics for individual parts are …...

We propose a novel self-training method for a parser which uses a lexicalised gram- mar and supertagger, focusing on increas- ing the speed of the parser rather than its accuracy. …...

We describe a new self-learning framework for parser lexicalisation that requires only a plain-text corpus of in-domain text. The method first creates augmented versions of de- pendency graphs by applying …...

AbstractNeural text classification models typically treat output labels as categorical variables that lack description and semantics. This forces their parametrization to be dependent on the label set size, and, hence, …...

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 …...

AbstractDisease is one of the fundamental entities in biomedical research. Recognizing such entities from biomedical text and then normalizing them to a standardized disease vocabulary offer a tremendous opportunity for …...

AbstractIn the field of Natural Language Processing (NLP), extracting method entities from biomedical text has been a challenging task. Scientific research papers commonly consist of complex keywords and domain-specific terminologies, …...