The Detection of Contradictory Claims in Biomedical Abstracts

Research claims in the biomedical domain are not always consistent, and may even be contradictory. This thesis explores contradictions between research claims in order to determine whether or not it is possible to develop a solution to automate the detection of such phenomena. Such a solution will help decision-makers, including researchers, to alleviate the effects of contradictory claims on their decisions. This study develops two methodologies to construct corpora of contradictions. The first methodology utilises systematic reviews to construct a manually-annotated corpus of contradictions. The second methodology uses a different approach to construct a corpus of contradictions which does not rely on human annotation. This methodology is proposed to overcome the limitations of the manual annotation approach. Moreover, this thesis proposes a pipeline to detect contradictions in abstracts. The pipeline takes a question and a list of research abstracts which may contain answers to it. The output of the pipeline is a list of sentences extracted from abstracts which answer the question, where each sentence is annotated with an assertion value with respect to the question. Claims which feature opposing assertion values are considered as potentially contradictory claims. The research demonstrates that automating the detection of contradictory claims in research abstracts is a feasible problem

Temporal disambiguation of relative temporal expressions in clinical texts using temporally fine-tuned contextual word embeddings.

Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Much of the current work in this field is focused on direct and explicit temporal expressions and identifying temporal relations. However, there is little work focused on relative temporal expressions, which can be difficult to normalize, but are vital to ordering events on a timeline. This work introduces a new temporal expression recognition and normalization tool, Chrono, that normalizes temporal expressions into both SCATE and TimeML schemes. Chrono advances clinical timeline extraction as it is capable of identifying more vague and relative temporal expressions than the current state-of-the-art and utilizes contextualized word embeddings from fine-tuned BERT models to disambiguate temporal types, which achieves state-of-the-art performance on relative temporal expressions. In addition, this work shows that fine-tuning BERT models on temporal tasks modifies the contextualized embeddings so that they achieve improved performance in classical SVM and CNN classifiers. Finally, this works provides a new tool for linking temporal expressions to events or other entities by introducing a novel method to identify which tokens an entire temporal expression is paying the most attention to by summarizing the attention weight matrices output by BERT models