The Belgian Salmonella surveillance programme 2005

The Belgian Salmonella Surveillance Programme on pig farms, organized by the Federal Agency for the Safety of the Food Chain, started in January 2005. The programme is built up in several stages. In the first stage the 10 % farms with the highest seroprevalence (number of positive samples per farm) are identified

Effectiveness of rotavirus vaccination in prevention of hospital admissions for rotavirus gastroenteritis among young children in Belgium : case-control study

Objective : To evaluate the effectiveness of rotavirus vaccination among young children in Belgium. Design : Prospective case-control study. Setting : Random sample of 39 Belgian hospitals, February 2008 to June 2010. Participants : 215 children admitted to hospital with rotavirus gastroenteritis confirmed by polymerase chain reaction and 276 age and hospital matched controls. All children were of an eligible age to have received rotavirus vaccination (that is, born after 1 October 2006 and aged >= 14 weeks). Main outcome measure : Vaccination status of children admitted to hospital with rotavirus gastroenteritis and matched controls. Results : 99 children (48%) admitted with rotavirus gastroenteritis and 244 (91%) controls had received at least one dose of any rotavirus vaccine (P= 12 months. The G2P[4] genotype accounted for 52% of cases confirmed by polymerase chain reaction with eligible matched controls. Vaccine effectiveness was 85% (64% to 94%) against G2P[4] and 95% (78% to 99%) against G1P[8]. In 25% of cases confirmed by polymerase chain reaction with eligible matched controls, there was reported co-infection with adenovirus, astrovirus and/or norovirus. Vaccine effectiveness against co-infected cases was 86% (52% to 96%). Effectiveness of at least one dose of any rotavirus vaccine (intention to vaccinate analysis) was 91% (82% to 95%). Conclusions : Rotavirus vaccination is effective for the prevention of admission to hospital for rotavirus gastroenteritis among young children in Belgium, despite the high prevalence of G2P[4] and viral co-infection

Deep Learning Based Multi-Label Text Classification of UNGA Resolutions

The main goal of this research is to produce a useful software for United Nations (UN), that could help to speed up the process of qualifying the UN documents following the Sustainable Development Goals (SDGs) in order to monitor the progresses at the world level to fight poverty, discrimination, climate changes. In fact human labeling of UN documents would be a daunting task given the size of the impacted corpus. Thus, automatic labeling must be adopted at least as a first step of a multi-phase process to reduce the overall effort of cataloguing and classifying. Deep Learning (DL) is nowadays one of the most powerful tools for state-of-the-art (SOTA) AI for this task, but very often it comes with the cost of an expensive and error-prone preparation of a training-set. In the case of multi-label text classification of domain-specific text it seems that we cannot effectively adopt DL without a big-enough domain-specific training-set. In this paper, we show that this is not always true. In fact we propose a novel method that is able, through statistics like TF-IDF, to exploit pre-trained SOTA DL models (such as the Universal Sentence Encoder) without any need for traditional transfer learning or any other expensive training procedure. We show the effectiveness of our method in a legal context, by classifying UN Resolutions according to their most related SDGs.Comment: 10 pages, 10 figures, accepted paper at ICEGOV 202

Automatic Synonymy Extraction : A Comparison of Syntactic Context Models

Distributional models of lexical semantics identify semantically similar words through contextual similarity. Previous studies have shown that syntactic contexts are especially good at finding (near) synonyms. In this paper, we compare models based on eight different syntactic dependency relations and we evaluate their separate and combined performance on a test set of Dutch nouns. Firstly, we analyze to what extent their results overlap. Secondly, we assess the overall performance of the models by looking at the average similarity of the words they return. And thirdly, we compare the specific semantic relations retrieved by the models. The analyses show that although models based on the subject and object relation give the most consistent results, it is the model based on adjective modification that gives the best results. It even outperforms the combined model at finding true synonyms

Size matters: tight and loose context definitions in English word space models

Word Space Models use distributional similarity between two words as a measure of their semantic similarity or relatedness. This distributional similarity, however, is influenced by the type of context the models take into account. Context definitions range on a continuum from tight to loose, depending on the size of the context window around the target or the order of the context words that are considered. This paper investigates whether two general ways of loosening the context definition — by extending the context size from one to ten words, and by taking into account second-order context words — produce equivalent results. In particular, we will evaluate the performance of the models in terms of their ability (1) to discover semantic word classes and (2) to mirror human associations.status: publishe

Applying word space models to sociolinguistics. Religion names before and after 9/11

© 2010 Walter de Gruyter GmbH & Co. KG, Berlin/New York. Researchers in disciplines like lexical semantics and critical discourse analysis are in need of a quantitative method that allows them to model the distribution of a word automatically. We advocate the use of word space models, a family of approaches that were developed in the context of computational linguistics and cognitive science, which represent the meaning of a word in terms of its contexts in a large corpus. In a case study on the use of religious terms before and after the attacks of September 11, 2001, we show how these models can be employed to determine the semantic similarity and relatedness between two words, and the factors that influence them. One of the patterns we uncover is the increased association between Islam and terrorism in Dutch newspaper articles after 9/11, a trend that is far less outspoken for Christianity. We also apply these new quantitative instruments to explore the differences in word use between the five newspapers in our corpus, and find a striking distinction between popular and quality newspapers.status: publishe

Putting things in order: first and second order context models for the calculation of semantic similarity

status: publishe

Automatic synonymy extraction. A Comparison of Syntactic Context Models

Distributional models of lexical semantics identify semantically similar words through contextual similarity. Previous studies have shown that syntactic contexts are especially good at finding (near) synonyms. In this paper, we compare models based on eight different syntactic dependency relations and we evaluate their separate and combined performance on a test set of Dutch nouns. Firstly, we analyze to what extent their results overlap. Secondly, we assess the overall performance of the models by looking at the average similarity of the words they return. And thirdly, we compare the specific semantic relations retrieved by the models. The analyses show that although models based on the subject and object relation give the most consistent results, it is the model based on adjective modification that gives the best results. It even outperforms the combined model at finding true synonyms.status: publishe

Modelling word similarity: an evaluation of automatic synonymy extraction algorithms

Vector-based models of lexical semantics retrieve semantically related words automatically from large corpora by exploiting the property that words with a similar meaning tend to occur in similar contexts. Despite their increasing popularity, it is unclear which kind of semantic similarity they actually capture and for which kind of words. In this paper, we use three vector-based models to retrieve semantically related words for a set of Dutch nouns and we analyse whether three linguistic properties of the nouns influence the results. In particular, we compare results from a dependency-based model with those from a 1st and 2nd order bag-of-words model and we examine the effect of the nouns’ frequency, semantic speficity and semantic class. We find that all three models find more synonyms for high-frequency nouns and those belonging to abstract semantic classses. Semantic specificty does not have a clear influence.status: publishe