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Objective To evaluate state-of-the-art unsupervised methods on the word sense disambiguation (WSD) task in the clinical domain. In particular, to compare graph-based approaches relying on a clinical knowledge base with bottom-up topic-modeling-based approaches. We investigate several enhancements to the topic-modeling techniques that use domain-specific(More)
—This paper explores the task of creating a timeline for historical Wikipedia articles, such as those describing wars, battles, and invasions. It focuses on extracting only the major events from the article, particularly those associated with an absolute date. Existing tools extract all possible events, while we write tools to identify time expressions and(More)
Our goal is to extract and display temporal entities in tex-tual documents. The task involves identification of all events in a document followed by identification of important events using a classifier. We present a user with the key events and their associated people, places, and organizations within a document in terms of a timeline and a map. For(More)
Traditional labeling theory usually contends that pathological labels contribute to pathology and benign labels help alleviate it. However, it is likely that the role of pathological labels as the cause of pathology has been overstated and overgeneralized. Family therapists have probably overused the practice of substituting a benign label for a(More)
Bayesian topic models have recently been shown to perform well in word sense induction (WSI) tasks. Such models have almost exclusively used bag-of-words features, and failed to attain improvement by including other feature types. In this paper, we investigate the impact of integrating syntactic and knowledge-based features and show that both parametric and(More)
The stigma associated with mental health issues makes face-to-face discussions with family members, friends, or medical professionals difficult for many people. In contrast, the Internet, due to its ubiquity and global outreach, is increasingly becoming a popular medium for distressed individuals to anonymously relate experiences. In this paper, we present(More)
We use Bayesian topic modeling techniques adapted to the task of unsupervised word sense induction on acronyms in clinical text and investigate (1) the amount of annotated data needed by such approaches to match the performance of the supervised sense disambiguation systems, and (2) feasibility of using an automatically collected silver standard for such(More)