BioNLP 2014



An ACL 2014 2-day Workshop associated with the SIGBIOMED special interest group.

Featuring a special track on NLP approaches for assessment of clinical conditions and a panel on shared tasks.

Baltimore, MD, June 26-27, 2014

Workshop web site:


Submission deadline Tuesday March 25, 2014, 11:59 PM Eastern US
Notification of acceptance Tuesday April 15, 2014
Camera-ready copy due from authors Friday April 25, 2014
WorkshopThursday - Friday June 26 - 27, 2014


Over the course of the past twelve years, the ACL BioNLP workshop associated with theSIGBIOMED special interest group has established itself as the primary venue for presenting foundational research in language processing for the biological and medical domains.

The workshop serves as both a venue for bringing together researchers in bio- and clinical NLP and exposing these researchers to the mainstream ACL research, and a venue for informing the mainstream ACL researchers about the fast growing and important domain.

The workshop will continue presenting work on a broad and interesting range of topics in NLP.

We especially encourage submissions on:

  • Entity identification and normalisation for a broad range of semantic categories
  • Species-independent gene normalisation
  • Extraction of complex relations
  • Discourse analysis
  • Anaphora resolution
  • Coreference resolution
  • Text mining
  • Summarization
    • Summarization/translation of clinical data for patients
  • Question Answering


The special track invites contributions from researchers working in NLP approachesfor the analysis of language samples to help in the assessment of clinical conditions.

Topics of relevance to the special track:

  • Development of linguistic resources in support of clinical applications research
  • Identification of clinical markers using NLP techniques
  • NLP techniques for assisting the development of intervention practices
  • Opinion papers related to pursuing this cross-disciplinary research
  • Automated approaches for the identification of clinical conditions from language samples

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