Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Mariem Ellouze"'
Publikováno v:
Jordanian Journal of Computers and Information Technology, Vol 8, Iss 4, Pp 370-387 (2022)
In written text, orthographic noise is a common concern for NLP, especially when operating social network comments and raw documents. This is mainly due to its orthographic conventions and morphological ambiguity. We propose to automatically normaliz
Externí odkaz:
https://doaj.org/article/7c78d7cd5c5641709c5e96085e0da766
Autor:
Mariem Ellouze, Nathália Buss Da Silva, Katia Rouzeau-Szynalski, Laura Coisne, Frédérique Cantergiani, József Baranyi
Publikováno v:
Frontiers in Microbiology, Vol 12 (2021)
Externí odkaz:
https://doaj.org/article/0e2a14312b3f4bb4af80e97f533c5d16
Autor:
Mariem Ellouze, Nathália Buss Da Silva, Katia Rouzeau-Szynalski, Laura Coisne, Frédérique Cantergiani, József Baranyi
Publikováno v:
Frontiers in Microbiology, Vol 12 (2021)
This study describes the simultaneous Bacillus cereus growth and cereulide formation, in culture medium and cereal-, dairy-, meat-, and vegetable-based food matrices. First, bacterial growth experiments were carried out under a wide range of temperat
Externí odkaz:
https://doaj.org/article/7fbd000915324743b51f056a184ae0d9
Publikováno v:
Journal of King Saud University: Computer and Information Sciences, Vol 29, Iss 2, Pp 147-155 (2017)
In this paper, we propose a method to disambiguate the output of a morphological analyzer of the Tunisian dialect. We test three machine-learning techniques that classify the morphological analysis of each word token into two classes: true and false.
Externí odkaz:
https://doaj.org/article/493ef502d1074491b17cecc0c7f6dc4e
Publikováno v:
Frontiers in Microbiology, Vol 8 (2017)
Predictive models of the growth of foodborne organisms are commonly based on data generated in laboratory medium. It is a crucial question how to apply the predictions to realistic food scenarios. A simple approach is to assume that the bias factor,
Externí odkaz:
https://doaj.org/article/d00b9122872e49c4bb25092bab13c6fe
Publikováno v:
Frontiers in Microbiology, Vol 8 (2017)
The maximum specific growth rates of 12 strains, pair-wise belonging to six groups of Bacillus cereus sensu lato, were fitted against temperature by a reparametrized version of the model of Ratkowsky et al. (1983). This way, the interpretation of the
Externí odkaz:
https://doaj.org/article/53254b56d3d64a8aa22f3f5c81357570
Publikováno v:
ACM Transactions on Asian and Low-Resource Language Information Processing.
Tokenization represents the way of segmenting a piece of text into smaller units called tokens. Since Arabic is an agglutinating language by nature, this treatment becomes a crucial preprocessing step for many Natural Language Processing (NLP) applic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7517dbcf17d1c24969044ee403bbc626
https://doi.org/10.1145/3599234
https://doi.org/10.1145/3599234
The aim of this research is to better understand public perceptions of COVID-19 pandemic patterns and to identify key themes of concern expressed by Tunisian dialect social media users throughout the epidemic. We collected around 23K comments written
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb526fe5fe460082fc79f22bfb4f44dc
https://doi.org/10.21203/rs.3.rs-2321298/v1
https://doi.org/10.21203/rs.3.rs-2321298/v1
Publikováno v:
Food Microbiology. 112:104230
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bf670522888a74659fd3e72bf88b3da5
https://doi.org/10.1016/j.fm.2023.104230
https://doi.org/10.1016/j.fm.2023.104230
Publikováno v:
Language Resources and Evaluation. 56:357-385
Sentence boundary detection (SBD) is an essential step for a very large number of natural language processing applications such as parsing, information retrieval, automatic summarization, machine translation, etc. In this paper, we tackle the problem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d6d091ef2850d3414565cb2cdd9d64d
https://doi.org/10.1007/s10579-021-09538-4
https://doi.org/10.1007/s10579-021-09538-4