Despite the recent success of deep neural networks in natural language processing and
other spheres of artificial intelligence, their interpretability remains a challenge. We analyze …

Negation is a universal linguistic phenomenon with a great qualitative impact on natural
language processing applications. The availability of corpora annotated with negation is …

Detecting negation and uncertainty is crucial for medical text mining applications; otherwise,
extracted information can be incorrectly identified as real or factual events. Although several …

We present the SFU Opinion and Comments Corpus (SOCC), a collection of opinion articles
and the comments posted in response to the articles. The articles include all the opinion …

Abstract In Electronic Health Records (EHRs), much of valuable information regarding
patients' conditions is embedded in free text format. Natural language processing (NLP) …

Drug development is an expensive and time-consuming process; these could be reduced if
the existing resources could be used to identify candidates for drug repurposing. This study …

Recognizing negative and speculative information is highly relevant for sentiment analysis.
This paper presents a machine‐learning approach to automatically detect this kind of …

Text analytics continue to proliferate as mass volumes of unstructured but highly useful data
are generated at unbounded rates. Vector space models for text data—in which documents …

When developing models for clinical information retrieval and decision support systems, the
discrete outcomes required for training are often missing. These labels need to be extracted …

Background: Negation and speculation are critical elements in natural language processing
(NLP)-related tasks, such as information extraction, as these phenomena change the truth …