Automatic generation of named entity taggers leveraging parallel corpora

The lack of hand curated data is a major impediment to developing statistical semantic processors for many of the world languages. A major issue of semantic processors in Nat- ural Language Processing (NLP) is that they require manually annotated data to perform accurately. Our work aims to address this issue by leveraging existing annotations and semantic processors from multiple source languages by projecting their annotations via statistical word alignments traditionally used in Machine Translation. Taking the Named Entity Recognition (NER) task as a use case of semantic processing, this work presents a method to automatically induce Named Entity taggers using parallel data, without any manual intervention. Our method leverages existing semantic processors and annotations to overcome the lack of annotation data for a given language. The intuition is to transfer or project semantic annotations, from multiple sources to a target language, by statistical word alignment methods applied to parallel texts (Och and Ney, 2000; Liang et al., 2006). The projected annotations can then be used to automatically generate semantic processors for the target language. In this way we would be able to provide NLP processors with- out training data for the target language. The experiments are focused on 4 languages: German, English, Spanish and Italian, and our empirical evaluation results show that our method obtains competitive results when compared with models trained on gold-standard out-of-domain data. This shows that our projection algorithm is effective to transport NER annotations across languages via parallel data thus providing a fully automatic method to obtain NER taggers for as many as the number of languages aligned via parallel corpora

Automatic generation of named entity taggers leveraging parallel corpora

The lack of hand curated data is a major impediment to developing statistical semantic processors for many of the world languages. A major issue of semantic processors in Nat- ural Language Processing (NLP) is that they require manually annotated data to perform accurately. Our work aims to address this issue by leveraging existing annotations and semantic processors from multiple source languages by projecting their annotations via statistical word alignments traditionally used in Machine Translation. Taking the Named Entity Recognition (NER) task as a use case of semantic processing, this work presents a method to automatically induce Named Entity taggers using parallel data, without any manual intervention. Our method leverages existing semantic processors and annotations to overcome the lack of annotation data for a given language. The intuition is to transfer or project semantic annotations, from multiple sources to a target language, by statistical word alignment methods applied to parallel texts (Och and Ney, 2000; Liang et al., 2006). The projected annotations can then be used to automatically generate semantic processors for the target language. In this way we would be able to provide NLP processors with- out training data for the target language. The experiments are focused on 4 languages: German, English, Spanish and Italian, and our empirical evaluation results show that our method obtains competitive results when compared with models trained on gold-standard out-of-domain data. This shows that our projection algorithm is effective to transport NER annotations across languages via parallel data thus providing a fully automatic method to obtain NER taggers for as many as the number of languages aligned via parallel corpora

Towards Knowledge-Grounded Counter Narrative Generation for Hate Speech

Tackling online hatred using informed textual responses - called counter narratives - has been brought under the spotlight recently. Accordingly, a research line has emerged to automatically generate counter narratives in order to facilitate the direct intervention in the hate discussion and to prevent hate content from further spreading. Still, current neural approaches tend to produce generic/repetitive responses and lack grounded and up-to-date evidence such as facts, statistics, or examples. Moreover, these models can create plausible but not necessarily true arguments. In this paper we present the first complete knowledge-bound counter narrative generation pipeline, grounded in an external knowledge repository that can provide more informative content to fight online hatred. Together with our approach, we present a series of experiments that show its feasibility to produce suitable and informative counter narratives in in-domain and cross-domain settings.Comment: To appear in "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL): Findings

Generating Counter Narratives against Online Hate Speech: Data and Strategies

Recently research has started focusing on avoiding undesired effects that come with content moderation, such as censorship and overblocking, when dealing with hatred online. The core idea is to directly intervene in the discussion with textual responses that are meant to counter the hate content and prevent it from further spreading. Accordingly, automation strategies, such as natural language generation, are beginning to be investigated. Still, they suffer from the lack of sufficient amount of quality data and tend to produce generic/repetitive responses. Being aware of the aforementioned limitations, we present a study on how to collect responses to hate effectively, employing large scale unsupervised language models such as GPT-2 for the generation of silver data, and the best annotation strategies/neural architectures that can be used for data filtering before expert validation/post-editing.Comment: To appear at ACL 2020 (long paper

Which Should Be Used First for ALK-Positive Non-Small-Cell Lung Cancer: Chemotherapy or Targeted Therapy? A Meta-Analysis of Five Randomized Trials

Background and objectives: Targeted therapy is widely used in the era of precision medicine. Whether the sequence in which targeted therapy and chemotherapy are performed matters, is however not known. We examined the impact of the sequential treatment of targeted therapy and chemotherapy among advanced anaplastic lymphoma kinase (ALK), non-small cell lung cancer (NSCLC) patients. Materials and Methods: Randomized controlled trials comparing the use of ALK inhibitors with chemotherapy were included in this meta-analysis. We estimated the hazard ratios (HRs) and 95% confidence intervals (CI), for progression-free survival (PFS) and overall survival (OS) from a random effects model. Two-sided statistical tests were used to determine the significance of these estimates. Results: In five eligible studies (1404 patients), ALK targeted therapy, in comparison with chemotherapy, had a significantly higher PFS (HR = 0.48; 95% CI, 0.42(-)0.55), but not significantly higher OS (HR = 0.88; 95% CI, 0.72(-)1.07). Crossover from chemotherapy to ALK inhibitors was allowed after progression in all trials. The sensitivity analysis of the use of ALK inhibitors as either the first- or second-line treatment, showed improvements in PFS but not in OS. Conclusions: Our results indicate that using targeted therapy first improved PFS, but that the sequence in which the treatments were performed did not cause a significant difference in overall survival

Empowering NGOs in Countering Online Hate Messages

Studies on online hate speech have mostly focused on the automated detection of harmful messages. Little attention has been devoted so far to the development of effective strategies to fight hate speech, in particular through the creation of counter-messages. While existing manual scrutiny and intervention strategies are time-consuming and not scalable, advances in natural language processing have the potential to provide a systematic approach to hatred management. In this paper, we introduce a novel ICT platform that NGO operators can use to monitor and analyze social media data, along with a counter-narrative suggestion tool. Our platform aims at increasing the efficiency and effectiveness of operators' activities against islamophobia. We test the platform with more than one hundred NGO operators in three countries through qualitative and quantitative evaluation. Results show that NGOs favor the platform solution with the suggestion tool, and that the time required to produce counter-narratives significantly decreases.Comment: Preprint of the paper published in Online Social Networks and Media Journal (OSNEM

Luteolin Suppresses Inflammatory Mediator Expression by Blocking the Akt/NFκB Pathway in Acute Lung Injury Induced by Lipopolysaccharide in Mice

Acute lung injury (ALI), instilled by lipopolysaccharide (LPS), is a severe illness with excessive mortality and has no specific treatment strategy. Luteolin is an anti-inflammatory flavonoid and widely distributed in the plants. Pretreatment with luteolin inhibited LPS-induced histological changes of ALI and lung tissue edema. In addition, LPS-induced inflammatory responses, including increased vascular permeability, tumor necrosis factor (TNF)-α and interleukin (IL)-6 production, and expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), were also reduced by luteolin in a concentration-dependent manner. Furthermore, luteolin suppressed activation of NFκB and its upstream molecular factor, Akt. These results suggest that the protection mechanism of luteolin is by inhibition of NFκB activation possibly via Akt