Browsing Data Science Institute (Conference Papers) by Author "Freitas, André"

Now showing items 1-4 of 4

    • SemEval-2017 Task 5: Fine-grained sentiment analysis on financial microblogs and news 

      Cortis, Keith; Freitas, André; Daudert, Tobias; Huerlimann, Manuela; Zarrouk, Manel; Handschuh, Siegfried; Davis, Brian (Association for Computational Linguistics, 2017-08-03)
      This paper discusses the “Fine-Grained Sentiment Analysis on Financial Microblogs and News” task as part of SemEval-2017, specifically under the “Detecting sentiment, humour, and truth” theme. This task contains two ...

    • SemR-11: a multi-lingual gold-standard for semantic similarity and relatedness for eleven languages 

      Barzegar, Siamak; Davis, Brian; Zarrouk, Manel; Handschuh, Siegfried; Freitas, André (European Language Resources Association, 2018-05-07)
      This work describes SemR-11, a multi-lingual dataset for evaluating semantic similarity and relatedness for 11 languages (German, French, Russian, Italian, Dutch, Chinese, Portuguese, Swedish, Spanish, Arabic and Persian). ...

    • The SSIX corpora: three gold standard corpora for sentiment analysis in English, Spanish and German financial microblogs 

      Gaillat, Thomas; Zarrouk, Manel; Freitas, André; Davis, Brian (European Language Resources Association, 2018-05-07)
      This paper introduces the three SSIX corpora for sentiment analysis. These corpora address the need to provide annotated data for supervised learning methods. They focus on stock-market related messages extracted from two ...

    • WWW'18 open challenge: financial opinion mining and question answering 

      Maia, Macedo; Handschuh, Siegfried; Freitas, André; Davis, Brian; McDermott, Ross; Zarrouk, Manel; Balahur, Alexandra (Association for Computing Machinery, 2018-04-23)
      The growing maturity of Natural Language Processing (NLP) techniques and resources is dramatically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The ...