711 research outputs found

    The Epistemological Foundations of Knowledge Representations

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    This paper looks at the epistemological foundations of knowledge representations embodied in retrieval languages. It considers questions such as the validity of knowledge representations and their effectiveness for the purposes of retrieval and automation. The knowledge representations it considers are derived from three theories of meaning that have dominated twentieth-century philosophy.published or submitted for publicatio

    Towards learning word representation

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    Continuous vector representations, as a distributed representations for words have gained a lot of attention in Natural Language Processing (NLP) field. Although they are considered as valuable methods to model both semantic and syntactic features, they still may be improved. For instance, the open issue seems to be to develop different strategies to introduce the knowledge about the morphology of words. It is a core point in case of either dense languages where many rare words appear and texts which have numerous metaphors or similies. In this paper, we extend a recent approach to represent word information. The underlying idea of our technique is to present a word in form of a bag of syllable and letter n-grams. More specifically, we provide a vector representation for each extracted syllable-based and letter-based n-gram, and perform concatenation. Moreover, in contrast to the previous method, we accept n-grams of varied length n. Further various experiments, like tasks-word similarity ranking or sentiment analysis report our method is competitive with respect to other state-of-theart techniques and takes a step toward more informative word representation construction

    Knowledge-driven entity recognition and disambiguation in biomedical text

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    Entity recognition and disambiguation (ERD) for the biomedical domain are notoriously difficult problems due to the variety of entities and their often long names in many variations. Existing works focus heavily on the molecular level in two ways. First, they target scientific literature as the input text genre. Second, they target single, highly specialized entity types such as chemicals, genes, and proteins. However, a wealth of biomedical information is also buried in the vast universe of Web content. In order to fully utilize all the information available, there is a need to tap into Web content as an additional input. Moreover, there is a need to cater for other entity types such as symptoms and risk factors since Web content focuses on consumer health. The goal of this thesis is to investigate ERD methods that are applicable to all entity types in scientific literature as well as Web content. In addition, we focus on under-explored aspects of the biomedical ERD problems -- scalability, long noun phrases, and out-of-knowledge base (OOKB) entities. This thesis makes four main contributions, all of which leverage knowledge in UMLS (Unified Medical Language System), the largest and most authoritative knowledge base (KB) of the biomedical domain. The first contribution is a fast dictionary lookup method for entity recognition that maximizes throughput while balancing the loss of precision and recall. The second contribution is a semantic type classification method targeting common words in long noun phrases. We develop a custom set of semantic types to capture word usages; besides biomedical usage, these types also cope with non-biomedical usage and the case of generic, non-informative usage. The third contribution is a fast heuristics method for entity disambiguation in MEDLINE abstracts, again maximizing throughput but this time maintaining accuracy. The fourth contribution is a corpus-driven entity disambiguation method that addresses OOKB entities. The method first captures the entities expressed in a corpus as latent representations that comprise in-KB and OOKB entities alike before performing entity disambiguation.Die Erkennung und Disambiguierung von EntitĂ€ten fĂŒr den biomedizinischen Bereich stellen, wegen der vielfĂ€ltigen Arten von biomedizinischen EntitĂ€ten sowie deren oft langen und variantenreichen Namen, große Herausforderungen dar. Vorhergehende Arbeiten konzentrieren sich in zweierlei Hinsicht fast ausschließlich auf molekulare EntitĂ€ten. Erstens fokussieren sie sich auf wissenschaftliche Publikationen als Genre der Eingabetexte. Zweitens fokussieren sie sich auf einzelne, sehr spezialisierte EntitĂ€tstypen wie Chemikalien, Gene und Proteine. Allerdings bietet das Internet neben diesen Quellen eine Vielzahl an Inhalten biomedizinischen Wissens, das vernachlĂ€ssigt wird. Um alle verfĂŒgbaren Informationen auszunutzen besteht der Bedarf weitere Internet-Inhalte als zusĂ€tzliche Quellen zu erschließen. Außerdem ist es auch erforderlich andere EntitĂ€tstypen wie Symptome und Risikofaktoren in Betracht zu ziehen, da diese fĂŒr zahlreiche Inhalte im Internet, wie zum Beispiel Verbraucherinformationen im Gesundheitssektor, relevant sind. Das Ziel dieser Dissertation ist es, Methoden zur Erkennung und Disambiguierung von EntitĂ€ten zu erforschen, die alle EntitĂ€tstypen in Betracht ziehen und sowohl auf wissenschaftliche Publikationen als auch auf andere Internet-Inhalte anwendbar sind. DarĂŒber hinaus setzen wir Schwerpunkte auf oft vernachlĂ€ssigte Aspekte der biomedizinischen Erkennung und Disambiguierung von EntitĂ€ten, nĂ€mlich Skalierbarkeit, lange Nominalphrasen und fehlende EntitĂ€ten in einer Wissensbank. In dieser Hinsicht leistet diese Dissertation vier HauptbeitrĂ€ge, denen allen das Wissen von UMLS (Unified Medical Language System), der grĂ¶ĂŸten und wichtigsten Wissensbank im biomedizinischen Bereich, zu Grunde liegt. Der erste Beitrag ist eine schnelle Methode zur Erkennung von EntitĂ€ten mittels Lexikonabgleich, welche den Durchsatz maximiert und gleichzeitig den Verlust in Genauigkeit und Trefferquote (precision and recall) balanciert. Der zweite Beitrag ist eine Methode zur Klassifizierung der semantischen Typen von Nomen, die sich auf gebrĂ€uchliche Nomen von langen Nominalphrasen richtet und auf einer selbstentwickelten Sammlung von semantischen Typen beruht, die die Verwendung der Nomen erfasst. Neben biomedizinischen können diese Typen auch nicht-biomedizinische und allgemeine, informationsarme Verwendungen behandeln. Der dritte Beitrag ist eine schnelle Heuristikmethode zur Disambiguierung von EntitĂ€ten in MEDLINE Kurzfassungen, welche den Durchsatz maximiert, aber auch die Genauigkeit erhĂ€lt. Der vierte Beitrag ist eine korpusgetriebene Methode zur Disambiguierung von EntitĂ€ten, die speziell fehlende EntitĂ€ten in einer Wissensbank behandelt. Die Methode wandelt erst die EntitĂ€ten, die in einem Textkorpus ausgedrĂŒckt aber nicht notwendigerweise in einer Wissensbank sind, in latente Darstellungen um und fĂŒhrt anschließend die Disambiguierung durch

    Semantic Representation of Physics Research Data

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    Improvements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing

    Understandings of language and cognition

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