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Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of(More)
Online health communities play an increasingly prevalent role for patients and are the source of a growing body of research. A lexicon that represents the sublanguage of an online community is an important resource to enable analysis and tool development over this data source. This paper investigates a method to generate a lexicon representative of the(More)
A large number of patients rely on online health communities to exchange information and psychosocial support with their peers. Examining participation in a community and its impact on members' behaviors and attitudes is one of the key open research questions in the field of study of online health communities. In this paper, we focus on a large public(More)
Speculations represent uncertainty toward certain facts. In clinical texts, identifying speculations is a critical step of natural language processing (NLP). While it is a nontrivial task in many languages, detecting speculations in Chinese clinical notes can be particularly challenging because word segmentation may be necessary as an upstream operation.(More)
Objectives : The Internet and social media are revolutionizing how social support is exchanged and perceived, making online health communities (OHCs) one of the most exciting research areas in health informatics. This paper aims to provide a framework for organizing research of OHCs and help identify questions to explore for future informatics research.(More)
This paper presents a system which adopts a standard sequence labeling technique for hedge detection and scope finding. For hedge detection, we formulate it as a hedge labeling problem, while for hedge scope finding, we use a two-step labeling strategy, one for hedge labeling and the other for scope finding. In particular, various kinds of syntactic(More)
Identifying topics of discussions in online health communities (OHC) is critical to various information extraction applications, but can be difficult because topics of OHC content are usually heterogeneous and domain-dependent. In this paper, we provide a multi-class schema, an annotated dataset, and supervised classifiers based on convolutional neural(More)
Hedge detection and scope finding are increasingly important tasks in information extraction , especially in the biomedical natural language processing community. In this paper, a novel approach detecting hedge cues and their scopes by sequence labeling is explored. It should be emphasized that syntactic dependencies are systematically exploited and(More)