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BEGIN:VEVENT
DTSTAMP:20260513T233648Z
DTSTART;VALUE=DATE-TIME:20180508T110000
DTEND;VALUE=DATE-TIME:20180508T120000
SUMMARY:Sophia Ananiadou: From text to knowledge: closing the gap
TZID:Europe/London
UID:20180508-8a17841b62f7555c0162f843165e4485@warwick.ac.uk
CREATED:20180424T152608Z
DESCRIPTION:Abstract: This seminar gives an overview of text mining metho
 ds that link knowledge with text. These methods support a number of appl
 ications\, e.g. semantic search\, screening for systematic reviews\, the
  development of pathway models\, etc. I will describe recent work carrie
 d out at the National Centre for Text Mining (NaCTeM) applied in areas o
 f biomedicine to reduce the time and expense devoted to manual literatur
 e mining. Pathway models are valuable resources that help us to understa
 nd the various mechanisms underpinning complex biological processes. The
 ir curation is typically carried out through manual inspection of the sc
 ientific literature\, a knowledge-intensive and laborious task. Text min
 ing methods are used to automate model reconstruction by increasing the 
 speed and reliability of discovery and extracting evidence from the lite
 rature . Complex information from the literature is automatically extrac
 ted and then mapped to reactions in existing pathway models. Information
  from the literature (events) can act as corroborative evidence of the v
 alidity of these reactions in a model or help to extend it. In addition\
 , by contextualising the textual evidence (extracting uncertainty\, nega
 tion)\, we can provide additional confidence measures for linking and ra
 nking information from the literature for model curation and ultimately 
 experimental design. In addition\, visual analytics methods can act as t
 he nexus between text mining methods and modellers by providing an inter
 active way to explore and analyse the statements linked with pathways. S
 hort Bio: Sophia Ananiadou is Professor in Computer Science\, School of 
 Computer Science\, The University of Manchester and Director of the Nati
 onal Centre for Text Mining. Since 2005\, she has successfully directed 
 NaCTeM to be currently a fully sustainable centre\, carrying out novel\,
  world-leading research on text mining that then informs the provision o
 f services\, tools\, resources and infrastructure to a variety of users 
 from translational medicine\, biology\, biodiversity\, humanities\, heal
 th\, and social sciences. Research she has led has advanced the state of
  the art in text mining and contributed in novel ways to: automatic extr
 action of terminology and term variation\; development of robust taggers
  for biomedical text\; automatic extraction of events and their interpre
 tation using machine learning methods\; development of large scale termi
 nological resources for biomedicine and biodiversity\; linking textual e
 vidence with metabolic and signaling pathways\; association mining and h
 ypothesis generation\; supporting the development of systematic reviews 
 using novel topic modeling and clustering methods and the development of
  interoperable text mining infrastructure to facilitate all the above ap
 plications (Argo). Her team achieved top performance is several text min
 ing challenges\, e.g. BioCreaTive (2010\, 2013\, 2015)\, BioNLP (2011\, 
 2013).
LOCATION:
CATEGORIES:
LAST-MODIFIED:20180424T152608Z
ORGANIZER;CN=Sara Kalvala:
END:VEVENT
END:VCALENDAR