Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein--protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence ``Protein A causes protein B to bind protein C&#39;&#39; can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP&#39;09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing.&nbsp; Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP&#39;09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP&#39;11 and BioNLP&#39;13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.</p

Biomedical Event Extraction with Machine Learning

Biomedical natural language processing (BioNLP) is a subfield of natural language processing, an area of computational linguistics concerned with developing programs that work with natural language: written texts and speech. Biomedical relation extraction concerns the detection of semantic relations such as protein-protein interactions (PPI) from scientific texts. The aim is to enhance information retrieval by detecting relations between concepts, not just individual concepts as with a keyword search. In recent years, events have been proposed as a more detailed alternative for simple pairwise PPI relations. Events provide a systematic, structural representation for annotating the content of natural language texts. Events are characterized by annotated trigger words, directed and typed arguments and the ability to nest other events. For example, the sentence “Protein A causes protein B to bind protein C” can be annotated with the nested event structure CAUSE(A, BIND(B, C)). Converted to such formal representations, the information of natural language texts can be used by computational applications. Biomedical event annotations were introduced by the BioInfer and GENIA corpora, and event extraction was popularized by the BioNLP'09 Shared Task on Event Extraction. In this thesis we present a method for automated event extraction, implemented as the Turku Event Extraction System (TEES). A unified graph format is defined for representing event annotations and the problem of extracting complex event structures is decomposed into a number of independent classification tasks. These classification tasks are solved using SVM and RLS classifiers, utilizing rich feature representations built from full dependency parsing. Building on earlier work on pairwise relation extraction and using a generalized graph representation, the resulting TEES system is capable of detecting binary relations as well as complex event structures. We show that this event extraction system has good performance, reaching the first place in the BioNLP'09 Shared Task on Event Extraction. Subsequently, TEES has achieved several first ranks in the BioNLP'11 and BioNLP'13 Shared Tasks, as well as shown competitive performance in the binary relation Drug-Drug Interaction Extraction 2011 and 2013 shared tasks. The Turku Event Extraction System is published as a freely available open-source project, documenting the research in detail as well as making the method available for practical applications. In particular, in this thesis we describe the application of the event extraction method to PubMed-scale text mining, showing how the developed approach not only shows good performance, but is generalizable and applicable to large-scale real-world text mining projects. Finally, we discuss related literature, summarize the contributions of the work and present some thoughts on future directions for biomedical event extraction. This thesis includes and builds on six original research publications. The first of these introduces the analysis of dependency parses that leads to development of TEES. The entries in the three BioNLP Shared Tasks, as well as in the DDIExtraction 2011 task are covered in four publications, and the sixth one demonstrates the application of the system to PubMed-scale text mining.Siirretty Doriast

Visual communication

In order to understand how landscape architects communicate and what visual language is being used when communicating design; we have gained vast knowledge in a number of areas. Visual communication touches many subjects such as perception, communication, language, marketing, image making, image analysis, rhetoric etc. How we perceive an image and what influences us is affected by many different things. Examples can include society or even the individual background. How an image appears is dependent upon what consistencies are being used. The image a landscape architect normally produces differs from the commercial image and art. In order to communicate most effectively it is important to be aware of what language is being used. Being clear and using a set language will minimise misunderstandings. Through a case study I have been looking at one company's opportunities and problems regarding visual communication and helped them to gain insight of what they were mediating. From this insight I have helped them improve their visual language and defined new goals to strive towards. Moreover I have been looking at the existing market through 14 Landscape Architect firms in order to gain an understanding of what visual languages are being used in today's Landscape Architecture environment.För att kunna förstå hur landskapsarkitekter kommunicerar och vilket visuellt språk de använder sig av vid förmedling av designarbete har jag fördjupat mig i ämnen som berör detta. Visuell kommunikation nuddar vid många ämnen exempelvis följande; perception; kommunikation; språkliga medel; marknadsföring; bilduppbyggnad; bildanalys; retorik osv. Hur vi uppfattar och läser av en bild och vad som påverkar oss som observatör är influerat av åtskilliga företeelser bland annat av vårt samhälle och vår bakgrund. Hur en bild ser ut avgörs av vilket sammanhang den används i. Illustrationerna en landskapsarkitekt normalt sätt skapar skiljer sig från reklamens bild och från konsten. För att kunna kommunicera på ett verkningsfullt sätt är det viktigt att man är medveten om vilket språk man använder. Ett tydligt språk minimerar missförstånd. Genom en fallstudie har jag fördjupat mig i ett landskapsarkitektkontors möjligheter och svårigheter inom visuell kommunikation och vidare ökat insikten i vad de idag förmedlar. Därefter klargjort åtråvärda mål samt arbetat med att förbättra och klargöra delar av deras visuella språk. Jag har även studerat 14 landskapsarkitektkontor som aktivt arbetar med visuell kommunikation för att få kunskap i vilka metoder och visualiseringsspråk som används i arkitektursammanhang idag

Older adults with autism spectrum disorders in Sweden: a register study of diagnoses, psychiatric care utilization and psychotropic medication of 601 individuals

In a Swedish sample of persons eligible for disability services and aged 55 years or older in 2012, persons (n = 601) with autism spectrum disorder diagnoses registered in specialist care were identified. Register data concerning diagnoses of other psychiatric disorders, psychiatric care, and psychiatric medication were reviewed. More than 60% had been in contact with psychiatric care. The majority had no intellectual disability (ID) diagnosis recorded during the study period. Apart from ID, affective disorders, anxiety and psychotic disorders were most commonly registered; alcohol/substance abuse disorders were uncommon. Psychotropic drug prescriptions were very common, especially in the ID group. Professionals need awareness of this vulnerable group; studies concerning their life circumstances and service requirements should be conducted

Upplevelser av kulturaktiviteters värde för personer inom palliativ vård

År 2009 antogs nya kulturpolitiska mål i Sverige med syfte att främja alla personers möjligheter till kulturupplevelser. Skapandet av dessa nya mål har sin grund i att det finns ett påvisat positivt samband mellan deltagande i kulturaktiviteter och hälsa. Arbetsterapeutisk litteratur betonar hur hälsan är kopplat till upplevd mening i sina aktiviteter. Aktivitetsvärdet som en aktivitet ges av individen är påverkat av hur meningsfull individen anser att aktiviteten är. Syftet med studien var att undersöka hur personer inom hospicevård upplever aktivitetsvärden av kulturaktiviteter. Studien genomfördes med en kvalitativ ansats. Urvalet bestod av tio vuxna personer som vårdades på hospice och deltog i minst en kulturaktivitet. Intervjuer genomfördes utifrån ValMO:s värdedimensioner som därefter analyserades utifrån en riktad innehållsanalys. Resultatet visade på värdeyttringar genom hela värdetriaden. Det symboliska värdet hade en majoritet utav värdeyttringarna. Slutsatsen av studien visar att kulturaktivitet med musik och skapande ger förutsättningar för deltagare att uppleva en meningsfull vardag

Learning to Extract Biological Event and Relation Graphs

Proceedings of the 17th Nordic Conference of Computational Linguistics NODALIDA 2009. Editors: Kristiina Jokinen and Eckhard Bick. NEALT Proceedings Series, Vol. 4 (2009), 18-25. © 2009 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/9206