Purpose Earlier work showed that IVIM-NETorig, an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to diffusion-weighted imaging (DWI). This study presents a substantially improved version, IVIM-NEToptim, characterizes its superior performance in pancreatic cancer patients. Method I...

OBJECTIVE To evaluate the expression of CD64 and CD163 on neutrophils and monocytes in SIRS with/without sepsis and to compare the diagnostic accuracy of CD64 and CD163 molecules expression determined as (1) mean fluorescence intensities (MFI) of CD64 and CD163; and (2) the ratio (index) of linearized MFI to the fluorescence signal of standardized beads. PATIENTS AND METHODS Fifty-six critica...

This work presents a supervised prepositional phrase (PP) attachment disambiguation system that uses contextualized distributional information as the distance metric for a nearest-neighbor classifier. Contextualized word vectors constructed from the GigaWord Corpus provide a method for implicit Word Sense Disambiguation (WSD), whose reliability helps this system outperform baselines and achieve...

We propose a supervised word sense disambiguation (WSD) method using tree-structured conditional random fields (TCRFs). By applying TCRFs to a sentence described as a dependency tree structure, we conduct WSD as a labeling problem on tree structures. To incorporate dependencies between word senses, we introduce a set of features on tree edges, in combination with coarse-grained tagsets, and sho...

This year we have participated in the first edition of Robust WSD task with the aim of investigating the performance of disambiguation tools applied to Information Retrieval (IR). The main interest of our experimentation is the characterization of queries where WSD is a useful tool. That is, which issues must be fulfilled by a query in order to apply an state-of-art WSD tool? After the interpre...

Natural Language Processing (NLP) of historical languages is an understudied area. Much previous work has focused on the problems of normalization and POS tagging. In contrast, we consider a new problem, word sense disambiguation (WSD). We provide a survey of previous work on processing of historical languages and discuss what we can and cannot apply to the problem of WSD, specifically WSD of O...

This article describes two different word sense disambiguation (WSD) systems, one applicable to parallel corpora and requiring aligned wordnets and the other one, knowledge poorer, albeit more relevant for real applications, relying on unsupervised learning methods and only monolingual data (text and wordnet). Comparing performances of word sense disambiguation systems is a very difficult evalu...