Best Paper Selection

Jing B, Xie P Xing E. On the automatic generation of medical imaging reports. Proc of ACL 2018. Melbourne, Australia; 2018. p. 2577-86 https://www.aclweb.org/anthology/P18-1240 Moradi M. CIBS: A biomedical text summarizer using topic-based sentence clustering J Biomed Inform 2018;88:53-61 https://www.sciencedirect.com/science/article/pii/S1532046418302156?via%3Dihub

Nudge the judge? Theorizing the interaction between heuristics, sentencing guidelines and sentence clustering

Although it has long been acknowledged that heuristics influence judicial decision making, researchers have yet to explore how sentencing guidelines might interact with heuristics to shape sentencing decisions. This article contributes to addressing this gap in the literature in three ways: first, by considering how heuristics might help produce the phenomenon of sentence clustering, in which a significant proportion of sentences are concentrated around a small number of outcomes; second, by reflecting on the role of sentencing guidelines as a feature of the environment within which sentencing decisions are made; and third, by analysing the guidelines from Minnesota and from England and Wales, theorizing how their content might interact with heuristics to make clustering more or less likely. Ultimately, we argue that sentencing guidelines likely affect the role played by heuristics in shaping sentencing decisions and, consequently, that their design should be informed by research evidence from the decision sciences.

Handling WSD using Hierarchical Clustering Algorithm with sentences

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%