Conventional word sense induction (WSI) methods usually represent each instance with discrete linguistic features or cooccurrence features, and train a model for each polysemous word individually. In this work, we propose to learn sense embeddings for the WSI task. In the training stage, our method induces several sense centroids (embedding) for each polysemous word. In the testing stage, our m...

Quantitative assessment of serial brain sections provides an objective measure of neurological events at cellular and molecular levels but is difficult to implement in experimental neuroscience laboratories because of variation from person-to-person and the time required for analysis. Whole slide imaging (WSI) technology, recently introduced for pathological diagnoses, offers an electronic envi...

The effect of dietary vitamin K intake on warfarin sensitivity is known only from case reports and few small clinical studies. We followed 50 patients commencing warfarin and consuming their regular diets (for 8 weeks) to study this relationship. A one-week recall dietary questionnaire was completed at weeks 2 and 8. Daily intake of nutrients and vitamin K was calculated from standard tables. W...

AbstractAssessing microsatellite stability status of a patient’s colorectal cancer is crucial in personalizing treatment regime. Recently, convolutional-neural-networks (CNN) combined with transfer-learning approaches were proposed to circumvent traditional laboratory testing for determining from hematoxylin and eosin stained biopsy whole slide images (WSI). However, the high resolution WSI pra...

Word Sense Induction (WSI) aims to automatically induce meanings of a polysemous word from unlabeled corpora. In this paper, we first propose a novel Bayesian parametric model to WSI. Unlike previous work, our research introduces a layer of hidden concepts and view senses as mixtures of concepts. We believe that concepts generalize the contexts, allowing the model to measure the sense similarit...

Lexical ambiguity, the ambiguity arising from a string with multiple meanings, is pervasive in language of all domains. Word sense disambiguation (WSD) and word sense induction (WSI) are the tasks of resolving this ambiguity. Applications in the clinical and biomedical domain focus on the potential disambiguation has for information extraction. Most approaches to the problem are unsupervised or...

Word sense induction (WSI) is the task aimed at automatically identifying the senses of words in texts, without the need for handcrafted resources or annotated data. Up till now, most WSI algorithms extract the different senses of a word ‘locally’ on a per-word basis, i.e. the different senses for each word are determined separately. In this paper, we compare the performance of such algorithms ...

Word Sense Induction (WSI) is the task of automatically inducing the different senses of a target word from unannotated text. Traditional approaches based on the vector space model (VSM) represent each context of a target word as a vector of selected features (e.g. the words occurring in the context). These approaches assume that the words occurring in the context are independent and do not exp...

This paper presents a novel feature descriptor and classification algorithms for automated scoring of HER2 in Whole Slide Images (WSI). Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level. We propose an efficient method based on characteristic curves which encode all relevant ...