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Coreference Resolution for Swedish and German using Distant Supervision

Coreference resolution is the identification of phrases that refer to the same entity in a text. Current techniques to solve coreferences use machine-learning algorithms, which require large annotated data sets. Such annotated Resources are no tavailable for most languages today. In this paper, we describe a method for solving coreferences for Swedish and German using distant supervision that does

Docforia: A Multilayer Document Model

In this paper, we describe Docforia, a multilayer document model and application programming interface (API) to store formatting, lexical, syntactic, and semantic annotations on Wikipedia and other kinds of text and visualize them. While Wikipedia has become a major NLP resource, its scale and heterogeneity makes it relatively difficult to do experimentations on the whole corpus. These experimentat

Langforia: Language pipelines for annotating large collections of documents.

In this paper, we describe Langforia, a multilingual processing pipeline to annotate texts with multiple layers: formatting, parts of speech, named entities, dependencies, semantic roles, and entity links. Langforia works as a web service, where the server hosts the language processing components and the client, the input and result visualization. To annotate a text or a Wikipedia page, the user c

Multilingual Supervision of Semantic Annotation

In this paper, we investigate the annotation projection of semantic units in a practical setting. Previous approaches have focused on using parallel corpora for semantic transfer. We evaluate an alternative approach using loosely parallel corpora that does not require the corpora to be exact translations of each other. We developed a method that transfers semantic annotations from one language to

Automatic Creation of Multilingual Semantic Networks from Wikipedia

This paper describes the automatic creation of semantic networks from Wikipedia. Following Lipczak et al. (2014), we constructed the graphs corresponding to the semantic networks by merging across languages the categories manually assigned by the users. This results in a network of related concepts for each entity of Wikipedia. We used these networks as a component of an entity linking system. the

Linking, Searching, and Visualizing Entities for the Swedish Wikipedia

In this paper, we describe a new system to extract, index, search, and visualize entities on Wikipedia. To carry out the extraction, we designed a high-performance entity linker and we used a document model to store the resulting linguistic annotations. The entity linker ,HERD, extracts the mentions from text using a string matching Engine and links the mto entities with a combination of rules, Pa

Docforia: A Multilayer Document Model

In this paper, we describe Docforia, a multilayer document model and application programming interface (API) to store formatting, lexical, syntactic, and semantic annotations on Wikipedia and other kinds of text and visualize them. While Wikipedia has become a major NLP resource, its scale and heterogeneity makes it relatively difficult to do experimentations on the whole corpus. These experimentat

Massive MIMO-based Localization and Mapping Exploiting Phase Information of Multipath Components

In this paper, we present a robust multipath-based localization and mapping framework that exploits the phases of specular multipath components (MPCs) using a massive multiple-input multipleoutput (MIMO) array at the base station. Utilizing the phase information related to the propagation distances of the MPCs enables the possibility of localization with extraordinary accuracy even with limited ba

Sparse optimization for nonlinear group delay mode estimation

Nonlinear group delay signals with frequency-varying characteristics are common in a wide variety of fields, for instance, structural health monitoring and fault diagnosis. For such applications, the signal is composed of multiple modes, where each mode may overlap in the frequency-domain. The resulting decomposition and forming of time-frequency representations of the nonlinear group delay modes

Fast and efficient minimal solvers for quadric based camera pose estimation

In this paper we address absolute camera pose estimation. An efficient (and standard) way to solve this problem, is to use sparse keypoint correspondences. In many cases point features are not available, or are unstable over time and viewing conditions. We propose a framework based on silhouettes of quadric surfaces, with special emphasis on cylinders. We provide mathematical analysis of the probl

MRI-targeted or standard biopsy for prostate-cancer diagnosis

BACKGROUND: Multiparametric magnetic resonance imaging (MRI), with or without targeted biopsy, is an alternative to standard transrectal ultrasonography-guided biopsy for prostate-cancer detection in men with a raised prostate-specific antigen level who have not undergone biopsy. However, comparative evidence is limited. METHODS: In a multicenter, randomized, noninferiority trial, we assigned men

Connecting a French Dictionary from the Beginning of the 20th Century to Wikidata

The Petit Larousse illustré is a French dictionary first published in 1905. Its division in two main parts on language and on history and geography corresponds to a major milestone in French lexicography as well as a repository of general knowledge from this period. Although the value of many entries from 1905 remains intact, some descriptions now have a dimension that is more historical than cont

Association of Short-Term Mortality of Venous Thromboembolism with Family History of Venous Thromboembolism and Charlson Comorbidity Index

Studies on short-term prognosis of venous thromboembolism (VTE) that take family history of VTE and Charlson Comorbidity Index (CCI) into account are sparse. The aim was to investigate the importance of family history of VTE and CCI for short-term mortality after a first episode of VTE. Using Swedish medical databases, we conducted a 90-day nationwide cohort study of 41,700 Swedish born patients w

Applications of Machine Learning on Natural Language Processing and Biomedical Data

Machine learning is ubiquitous in today’s society, with promising applicationsin the field of natural language processing (NLP), so that computers can handlehuman language better, and within the medical community, with the promiseof better treatments. Machine learning can be seen as a subfield of artificialintelligence (AI), where AI is used to describe a machine that mimics cognitivefunctions tha

Predicting the Outcome for Patients in a Heart Transplantation Queue using Deep Learning

Heart transplantations have made it possible to extend the median survival time to 12 years for patients with end-stage heart diseases. This operation is unfortunately limited by the availability of donor organs and patients have to wait on average about 200 days in a waiting list before being operated. This waiting time varies considerably across the patients. In this paper, we studied the outcom