Dublin City University

About: Dublin City University is a education organization based out in Dublin, Ireland. It is known for research contribution in the topics: Context (language use) & Machine translation. The organization has 5904 authors who have published 17178 publications receiving 389376 citations. The organization is also known as: National Institute for Higher Education, Dublin & DCU.

Etiologic Heterogeneity Among Non-Hodgkin Lymphoma Subtypes: The InterLymph Non-Hodgkin Lymphoma Subtypes Project

Abstract: Non-Hodgkin lymphoma (NHL) is the most common hematologic malignancy and the fifth most common type of cancer in more developed regions of the world (1). Numerous NHL subtypes with distinct combinations of morphologic, immunophenotypic, genetic, and clinical features are currently recognized (2,3). The incidence of NHL subtypes varies substantially by age, sex, and race/ethnicity (4–7). However, the etiological implications of this biological, clinical, and epidemiological diversity are incompletely understood. The importance of investigating etiology by NHL subtype is clearly supported by research on immunosuppression, infections, and autoimmune diseases, which are the strongest and most established risk factors for NHL. Studies of solid organ transplant recipients and individuals infected with HIV demonstrate that risks are markedly increased for several—but not all—NHL subtypes (8–13). Some infections and autoimmune diseases are associated with a single specific subtype [eg, human T-cell lymphotropic virus, type I (HTLV-I) with adult T-cell leukemia/lymphoma (14), celiac disease with enteropathy-type peripheral T-cell lymphoma (PTCL) (15–17)], whereas others [eg, Epstein–Barr virus, hepatitis C virus (HCV), Sjogren’s syndrome (18–21)] have been associated with multiple subtypes. In the last two decades, reports from individual epidemiological studies of NHL have suggested differences in risks among NHL subtypes for a wide range of risk factors, but most studies have lacked the statistical power to assess any differences quantitatively and have not systematically evaluated combinations of subtypes. One study assessed multiple risk factors and found support for both etiologic commonality and heterogeneity for NHL subtypes, with risk factor patterns suggesting that immune dysfunction is of greater etiologic importance for diffuse large B-cell lymphoma (DLBCL) and marginal zone lymphoma than for chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) and follicular lymphoma (22). However, that analysis was limited to approximately 1300 NHL cases and considered only the four most common NHL subtypes. Pooling data from multiple studies through the International Lymphoma Epidemiology Consortium (InterLymph) have provided substantial insight into associations between specific risk factors and NHL subtypes, with evidence that family history of hematologic malignancy, autoimmune diseases, atopic conditions, lifestyle factors (smoking, alcohol, anthropometric measures, and hair dye use), and sun exposure are associated with NHL risk (19,21,23–32). However, no previous study has compared patterns of risk for a range of exposures for both common and rarer NHL subtypes. We undertook the InterLymph NHL Subtypes Project, a pooled analysis of 20 case–control studies including 17 471 NHL cases and 23 096 controls, to advance understanding of NHL etiology by investigating NHL subtype-specific risks associated with medical history, family history of hematologic malignancy, lifestyle factors, and occupation. The detailed risk factor profiles for each of 11 NHL subtypes appear in this issue (15–17,33–40). In this report, we assess risk factor heterogeneity among the NHL subtypes and identify subtypes that have similar risk factor profiles.

Code Mixing: A Challenge for Language Identification in the Language of Social Media

Abstract: In social media communication, multilingual speakers often switch between languages, and, in such an environment, automatic language identification becomes both a necessary and challenging task. In this paper, we describe our work in progress on the problem of automatic language identification for the language of social media. We describe a new dataset that we are in the process of creating, which contains Facebook posts and comments that exhibit code mixing between Bengali, English and Hindi. We also present some preliminary word-level language identification experiments using this dataset. Different techniques are employed, including a simple unsupervised dictionary-based approach, supervised word-level classification with and without contextual clues, and sequence labelling using Conditional Random Fields. We find that the dictionary-based approach is surpassed by supervised classification and sequence labelling, and that it is important to take contextual clues into consideration.

"I'm eating a sandwich in Glasgow": modeling locations with tweets

Abstract: Social media such as Twitter generate large quantities of data about what a person is thinking and doing in a particular location. We leverage this data to build models of locations to improve our understanding of a user's geographic context. Understanding the user's geographic context can in turn enable a variety of services that allow us to present information, recommend businesses and services, and place advertisements that are relevant at a hyper-local level.In this paper we create language models of locations using coordinates extracted from geotagged Twitter data. We model locations at varying levels of granularity, from the zip code to the country level. We measure the accuracy of these models by the degree to which we can predict the location of an individual tweet, and further by the accuracy with which we can predict the location of a user. We find that we can meet the performance of the industry standard tool for predicting both the tweet and the user at the country, state and city levels, and far exceed its performance at the hyper-local level, achieving a three- to ten-fold increase in accuracy at the zip code level.