#SEMEVAL 2017 TASK3 HOW TO#
Note: For instructions on how to submit system results to any of these tasks, please check this page. Subtask E (English): Multi-Domain Duplicate Question Detection.
Subtask D (Arabic): Rerank correct answers for a new Question.Subtask C (English): Question-External Comment Similarity.Subtask B (English): Question-Question Similarity.Subtask A (English): Question-Comment Similarity.Our proposed system is ranked 6th and 8th in the multilingual and code-mixed MultiCoNER's tracks respectively. Furthermore, we use a self-training mechanism to generate weakly-annotated data from a large unlabeled dataset. In addition to the CRF-based token classification layer, we incorporate a span classification loss to recognize named entities spans. We approach the complex NER for multilingual and code-mixed queries, by relying on the contextualized representation provided by the multilingual Transformer XLM-RoBERTa. our submitted system to the Multilingual Complex Named Entity Recognition (MultiCoNER) shared task. Besides, real-world queries are mostly malformed, as they can be code-mixed or multilingual, among other scenarios.
This is due to the complexity and ambiguity of named entities that appear in various contexts such as short input sentences, emerging entities, and complex entities. The proposed semantic linking framework and the story ranking method have been tested on a set of 60 hours open-benchmark TRECVID video data, and very satisfactory results for both tasks have been obtained.īuilding real-world complex Named Entity Recognition (NER) systems is a challenging task. The output results of the story linking are further used in a story ranking task, which indicate the interesting level of the stories. The language similarity is expressed in terms of the normalized text similarity between the stories' keywords. The facial key-frames are linked based on the analysis of the extended facial regions, and the non-facial key-frames are correlated using the global Affine matching. Visually, each news story is represented by a set of key-frames with or without detected faces. The semantic linkage between the news stories is reflected in combination of both of their visual content and their spoken language content. In this paper, we propose a concept tracking method, which links news stories on the same topic across multiple sources. Information linkage is becoming more and more important in this digital age. We find that our techniques outperform the state of the art with statistical significance and are more discriminating when faced with a diverse collection of documents. We evaluate these algorithmic weightings using human judgments to determine similarity. News articles are particularly challenging due to the prevalence of syndicated articles, where very similar articles are run with different headlines and surrounded by different HTML markup and site templates. We experiment on a subset of a publicly available web collection that is comprised solely of documents from news web sites.
We expand on previous work and improve the performance of near-duplicate document detection by weighting the phrases in a sliding window based on the term frequency within the document of terms in that window and inverse document frequency of those phrases. By detecting near-duplicates, we can offer users only those stories with content materially different from previously-viewed versions of the story. These stories often consist primarily of syndicated information, with local replacement of headlines, captions, and the addition of locally-relevant content. In this paper, we investigate near-duplicate detection, particularly looking at the detection of evolving news stories. To this end, we propound the idea of the inclusion of multilingual debunk search in the fact-checking pipeline.
#SEMEVAL 2017 TASK3 MANUAL#
Furthermore, as manual fact-checking is an onerous task in itself, therefore debunking similar claims recurrently is leading to a waste of resources. We also find that misinformation involving general medical advice has spread across multiple countries and hence has the highest proportion of false COVID-19 narratives that keep being debunked. The spatiotemporal analysis reveals that similar or nearly duplicate false COVID-19 narratives have been spreading in multifarious modalities on various social media platforms in different countries. This study is about COVID-19 debunks published in multiple languages by different fact-checking organisations, sometimes as far as several months apart, despite the fact that the claim has already been fact-checked before. During this time, a number of manual fact-checking initiatives have emerged to alleviate the spread of dis/mis-information. The onset of the Coronavirus disease 2019 (COVID-19) pandemic instigated a global infodemic that has brought unprecedented challenges for society as a whole.
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