OGER: OntoGene’s Entity Recogniser in the BeCalm TIPS Task

We present OGER, an annotation service built on top of OntoGene’s biomedical entity recognition system, which participates in the TIPS task (technical interoperability and performance of annotation servers) of the BeCalm (biomedical annotation metaserver) challenge. The annotation server is a web application tailored to the needs of the task, using an existing biomedical entity recognition suite. The core annotation module uses a knowledge-based strategy for term matching and entity linking. The server’s architecture allows parallel processing of annotation requests for an arbitrary number of documents from mixed sources. In the discussion, we show that network latency is responsible for significant overhead in the measurement of processing time. We compare the preliminary key performance indicators with an analysis drawn from the server’s log messages. We conclude that our annotation server is ready for the upcoming phases of the TIPS task

Ranking Interactions for a Curation Task

One of the key pieces of information which biomedical text mining systems are expected to extract from the literature are interactions among different types of biomedical entities (proteins, genes, diseases, drugs, etc.). Different types of entities might be considered, for example protein-protein interactions have been extensively studied as part of the Bio Creative competitive evaluations. However, more complex interactions such as those among genes, drugs, and diseases are increasingly of interest. Different databases have been used as reference for the evaluation of extraction and ranking techniques. The aim of this paper is to describe a machine-learning based reranking approach for candidate interactions extracted from the literature. The results are evaluated using data derived from the Pharm GKB database. The importance of a good ranking is particularly evident when the results are applied to support human curators

The Birman-Schwinger Operator for a Parabolic Quantum Well in a Zero-Thickness Layer in the Presence of a Two-Dimensional Attractive Gaussian Impurity

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TX Task: Automatic detection of focus organisms in biomedical publications

In biomedical information extraction (IE), a central problem is the disambiguation of ambiguous names for domain specific entities, such as proteins, genes, etc. One important dimension of ambiguity is the organism to which the entities belong: in order to disambiguate an ambiguous entity name (e.g. a protein), it is often necessary to identify the specific organism to which it refers. In this paper we present an approach to the detection and disambiguation of the focus organism(s), i.e. the organism(s) which are the subject of the research described in scientific papers, which can then be used for the disambiguation of other entities. The results are evaluated against a gold standard derived from IntAct annotations. The evaluation suggests that the results may already be useful within a curation environment and are certainly a baseline for more complex approaches

Detection of interaction articles and experimental methods in biomedical literature

Background: This article describes the approaches taken by the OntoGene group at the University of Zurich in dealing with two tasks of the BioCreative III competition: classification of articles which contain curatable protein- protein interactions (PPI-ACT) and extraction of experimental methods (PPI-IMT). Results: Two main achievements are described in this paper: (a) a system for document classification which crucially relies on the results of an advanced pipeline of natural language processing tools; (b) a system which is capable of detecting all experimental methods mentioned in scientific literature, and listing them with a competitive ranking (AUC iP/R > 0.5). Conclusions: The results of the BioCreative III shared evaluation clearly demonstrate that significant progress has been achieved in the domain of biomedical text mining in the past few years. Our own contribution, together with the results of other participants, provides evidence that natural language processing techniques have become by now an integral part of advanced text mining approaches

Knowledge mining over scientific literature and technical documentation

Abstract This dissertation focuses on the extraction of information implicitly encoded in domain descriptions (technical terminology and related items) and its usage within a restricted-domain question answering system (QA). Since different variants of the same term can be used to refer to the same domain entity, it is necessary to recognize all possible forms of a given term and structure them, so that they can be used in the question answering process. The knowledge about domain descriptions and their mutual relations is leveraged in an extension to an existing QA system, aimed at the technical maintenance manual of a well-known commercial aircraft. The original version of the QA system did not make use of domain descriptions, which are the novelty introduced by the present work. The explicit treatment of domain descriptions provided considerable gains in terms of efficiency, in particular in the process of analysis of the background document collection. Similar techniques were later applied to another domain (biomedical scientific literature), focusing in particular on protein- protein interactions. This dissertation describes in particular: (1) the extraction of domain specific lexical items which refer to entities of the domain; (2) the detection of relationships (like synonymy and hyponymy) among such items, and their organization into a conceptual structure; (3) their usage within a domain restricted question answering system, in order to facilitate the correct identification of relevant answers to a query; (4) the adaptation of the system to another domain, and extension of the basic hypothesis to tasks other than question answering. Zusammenfassung Das Thema dieser Dissertation ist die Extraktion von Information, welche implizit in technischen Terminologien und ähnlichen Ressourcen enthalten ist, sowie ihre Anwendung in einem Antwortextraktionssystem (AE). Da verschiedene Varianten desselben Terms verwendet werden können, um auf den gleichen Begriff zu verweisen, ist die Erkennung und Strukturierung aller möglichen Formen Voraussetzung für den Einsatz in einem AE-System. Die Kenntnisse über Terme und deren Relationen werden in einem AE System angewandt, welches auf dem Wartungshandbuch eines bekannten Verkehrsflugzeug fokussiert. Die ursprüngliche Version des Systems hatte keine explizite Behandlung von Terminologie. Die explizite Behandlung von Terminologie lieferte eine beachtliche Verbesserung der Effizienz des Systems, insbesondere was die Analyse der zugrundeliegenden Dokumentensammlung betrifft. Ähnliche Methodologien wurden später auf einer anderen Domäne angewandt (biomedizinische Literatur), mit einen besonderen Fokus auf Interaktionen zwischen Proteinen. Diese Dissertation beschreibt insbesondere: (1) die Extraktion der Terminologie (2) die Identifikation der Relationen zwischen Termen (wie z.B. Synonymie und Hyponymie) (3) deren Verwendung in einen AE System (4) die Portierung des Systems auf eine andere Domäne