Which Melbourne? Augmenting geocoding with maps

The purpose of text geolocation is to associate geographic information contained in a document with a set (or sets) of coordinates, either implicitly by using linguistic features and/or explicitly by using geographic metadata combined with heuristics. We introduce a geocoder (location mention disambiguator) that achieves state-of-the-art (SOTA) results on three diverse datasets by exploiting the implicit lexical clues. Moreover, we propose a new method for systematic encoding of geographic metadata to generate two distinct views of the same text. To that end, we introduce the Map Vector (MapVec), a sparse representation obtained by plotting prior geographic probabilities, derived from population figures, on a World Map. We then integrate the implicit (language) and explicit (map) features to significantly improve a range of metrics. We also introduce an open-source dataset for geoparsing of news events covering global disease outbreaks and epidemics to help future evaluation in geoparsing

Vancouver Welcomes You! Minimalist Location Metonymy Resolution

Named entities are frequently used in a metonymic manner. They serve as references to related entities such as people and organisations. Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing. Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources. We show how a minimalist neural approach combined with a novel predicate window method can achieve state-of-the-art results on the SemEval 2007 task on Metonymy Resolution. Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines

What’s missing in geographical parsing?

Geographical data can be obtained by converting place names from free-format text into geographical coordinates. The ability to geo-locate events in textual reports represents a valuable source of information in many real-world applications such as emergency responses, real-time social media geographical event analysis, understanding location instructions in auto-response systems and more. However, geoparsing is still widely regarded as a challenge because of domain language diversity, place name ambiguity, metonymic language and limited leveraging of context as we show in our analysis. Results to date, whilst promising, are on laboratory data and unlike in wider NLP are often not cross-compared. In this study, we evaluate and analyse the performance of a number of leading geoparsers on a number of corpora and highlight the challenges in detail. We also publish an automatically geotagged Wikipedia corpus to alleviate the dearth of (open source) corpora in this domain.We gratefully acknowledge the funding support of the Natural Environment Research Council (NERC) Ph.D. Studentship NE/M009009/1 (MG) and EPSRC (NC and NL: Grant No. EP/M005089/1

Hemophagocytic lymphohistiocytosis in critically ill patients: diagnostic reliability of HLH-2004 criteria and HScore

Background: Hemophagocytic lymphohistiocytosis (HLH) is a rare though often fatal hyperinflammatory syndrome mimicking sepsis in the critically ill. Diagnosis relies on the HLH-2004 criteria and HScore, both of which have been developed in pediatric or adult non-critically ill patients, respectively. Therefore, we aimed to determine the sensitivity and specificity of HLH-2004 criteria and HScore in a cohort of adult critically ill patients. Methods: In this further analysis of a retrospective observational study, patients ≥ 18 years admitted to at least one adult ICU at Charité - Universitätsmedizin Berlin between January 2006 and August 2018 with hyperferritinemia of ≥ 500 μg/L were included. Patients' charts were reviewed for clinically diagnosed or suspected HLH. Receiver operating characteristics (ROC) analysis was performed to determine prediction accuracy. Results: In total, 2623 patients with hyperferritinemia were included, of whom 40 patients had HLH. We found the best prediction accuracy of HLH diagnosis for a cutoff of 4 fulfilled HLH-2004 criteria (95.0% sensitivity and 93.6% specificity) and HScore cutoff of 168 (100% sensitivity and 94.1% specificity). By adjusting HLH-2004 criteria cutoffs of both hyperferritinemia to 3000 μg/L and fever to 38.2 °C, sensitivity and specificity increased to 97.5% and 96.1%, respectively. Both a higher number of fulfilled HLH-2004 criteria [OR 1.513 (95% CI 1.372-1.667); p < 0.001] and a higher HScore [OR 1.011 (95% CI 1.009-1.013); p < 0.001] were significantly associated with in-hospital mortality. Conclusions: An HScore cutoff of 168 revealed a sensitivity of 100% and a specificity of 94.1%, thereby providing slightly superior diagnostic accuracy compared to HLH-2004 criteria. Both HLH-2004 criteria and HScore proved to be of good diagnostic accuracy and consequently might be used for HLH diagnosis in critically ill patients. Clinical trial registration: The study was registered with www.ClinicalTrials.gov (NCT02854943) on August 1, 2016

Detecting natural disasters, damage, and incidents in the wild

Responding to natural disasters, such as earthquakes, floods, and wildfires, is a laborious task performed by on-the-ground emergency responders and analysts. Social media has emerged as a low-latency data source to quickly understand disaster situations. While most studies on social media are limited to text, images offer more information for understanding disaster and incident scenes. However, no large-scale image datasets for incident detection exists. In this work, we present the Incidents Dataset, which contains 446,684 images annotated by humans that cover 43 incidents across a variety of scenes. We employ a baseline classification model that mitigates false-positive errors and we perform image filtering experiments on millions of social media images from Flickr and Twitter. Through these experiments, we show how the Incidents Dataset can be used to detect images with incidents in the wild. Code, data, and models are available online at http://incidentsdataset.csail.mit.edu.Comment: ECCV 202

Research data supporting "Which Melbourne? Augmenting Geocoding with Maps"

Please unzip the files and read the README file for more instructions. Also visit my GitHub account for more information (milangritta

Research data supporting "Vancouver Welcomes You! Minimalist Location Metonymy Resolution"

Complete supporting/replication data and code for the ACL Publication. The paper was published in August 2017 at www.acl2017.orgWe gratefully acknowledge the funding support of the Natural Environment Research Council (NERC) PhD Studentship NE/M009009/1 (Milan Gritta, DREAM CDT), EPSRC (Nigel Collier and Nut Limsopatham - Grant No. EP/M005089/1) and MRC (Mohammad Taher Pilehvar) Grant No. MR/M025160/1 for PheneBank

Research data supporting "What’s missing in geographical parsing?"

Full code and data required for replication and experimentation

Hipk Is Required For JAK/STAT Activity during Development and Tumorigenesis

Drosophila&nbsp;has been instrumental as a model system in studying signal transduction and revealing molecular functions in development and human diseases. A point mutation in the Drosophila Janus kinase JAK (called&nbsp;hop) causes constitutive activation of the JAK/STAT pathway. We provide robust genetic evidence that the Homeodomain interacting protein kinase (Hipk) is required for endogenous JAK/STAT activity. Overexpression of Hipk can phenocopy the effects of overactive JAK/STAT mutations and lead to melanized tumors, and loss of Hipk can suppress the effects of hyperactive JAK/STAT. Further, the loss of the pathway effector Stat92E can suppress Hipk induced overgrowth. Interaction studies show that Hipk can physically interact with Stat92E and regulate Stat92E subcellular localization. Together our results show that Hipk is a novel factor required for effective JAK/STAT signaling