University of Amsterdam at TREC 2019:Complex Answer Retrieval Track

This paper documents the University of Amsterdam’s participation in the TREC 2019 Complex Answer Retrieval Track. This is the first year we actively participate in TREC CAR, attracted by the introduction to the limited “budget” of 20 passages per heading in the outline. We conducted initial exploratory experiments on making each heading contain a unique set of passages within the outline, and even do this hierarchical for each subtree and main title/article level, hence remove any redundancy between passages for different “queries” within the same title. We experimented with top-down and bottom-up filtering approaches. At the time of writing we are still in the process of analyzing the results. Some initial observations are the following. First, the restriction makes the task very challenging, as assigning any passage to the right heading in the outline is highly non-trivial. Qualitative analysis shows that our simple heuristics often make a different decision than the editorial judges on the heading under which a passage relevant to the title’s topic is assigned. Second, the fraction of judged and relevant passages per individual query or leave node is very small, making it hard to draw any definite conclusions on our experiments, and also resulting in a too small recall base to evaluate our non-pooled runs in a meaningful way. Third, when aggregating all qrels and runs to the title level, there is reasonable effectiveness of the underlying BM25 rankings, showing that the underlying passage ranking is not unreasonable, and that the hard and interesting problem is in the exact assignment of passages to the “right” headings

University of Amsterdam at TREC 2019:Complex Answer Retrieval Track

This paper documents the University of Amsterdam’s participation in the TREC 2019 Complex Answer Retrieval Track. This is the first year we actively participate in TREC CAR, attracted by the introduction to the limited “budget” of 20 passages per heading in the outline. We conducted initial exploratory experiments on making each heading contain a unique set of passages within the outline, and even do this hierarchical for each subtree and main title/article level, hence remove any redundancy between passages for different “queries” within the same title. We experimented with top-down and bottom-up filtering approaches. At the time of writing we are still in the process of analyzing the results. Some initial observations are the following. First, the restriction makes the task very challenging, as assigning any passage to the right heading in the outline is highly non-trivial. Qualitative analysis shows that our simple heuristics often make a different decision than the editorial judges on the heading under which a passage relevant to the title’s topic is assigned. Second, the fraction of judged and relevant passages per individual query or leave node is very small, making it hard to draw any definite conclusions on our experiments, and also resulting in a too small recall base to evaluate our non-pooled runs in a meaningful way. Third, when aggregating all qrels and runs to the title level, there is reasonable effectiveness of the underlying BM25 rankings, showing that the underlying passage ranking is not unreasonable, and that the hard and interesting problem is in the exact assignment of passages to the “right” headings

University of Amsterdam at CLEF 2020:Notebook for the Touché Lab on Argument Retrieval at CLEF 2020

This paper documents the University of Amsterdam’s participation in CLEF 2020 Touché Track. This is the first year this track has been introduced at CLEF, and we were attracted to participate in it due to its potentialities for Parliamentary debates we are currently working on. This track consists of two tasks: Conversational Argument Retrieval and Comparative Argument Retrieval.We submitted a run to both tasks. For the first task, we used a combination of the traditional BM25 model and learning to rank models. BM25 model helps to retrieve relevant arguments, and learning to rank model helps to re-rank the list and put stronger arguments on top of the list. For the second task, Comparative Argument Retrieval, we proposed a pipeline to re-rank documents retrieved from Clueweb using three features: PageRank scores, web domains, and argumentativeness. Preliminary results on 5 queries have shown that this heuristic pipeline may help to achieve a balance among three important dimensions: relevance, trustworthiness, and argumentativeness

University of Amsterdam at TREC 2019:Complex Answer Retrieval Track

This paper documents the University of Amsterdam’s participation in the TREC 2019 Complex Answer Retrieval Track. This is the first year we actively participate in TREC CAR, attracted by the introduction to the limited “budget” of 20 passages per heading in the outline. We conducted initial exploratory experiments on making each heading contain a unique set of passages within the outline, and even do this hierarchical for each subtree and main title/article level, hence remove any redundancy between passages for different “queries” within the same title. We experimented with top-down and bottom-up filtering approaches. At the time of writing we are still in the process of analyzing the results. Some initial observations are the following. First, the restriction makes the task very challenging, as assigning any passage to the right heading in the outline is highly non-trivial. Qualitative analysis shows that our simple heuristics often make a different decision than the editorial judges on the heading under which a passage relevant to the title’s topic is assigned. Second, the fraction of judged and relevant passages per individual query or leave node is very small, making it hard to draw any definite conclusions on our experiments, and also resulting in a too small recall base to evaluate our non-pooled runs in a meaningful way. Third, when aggregating all qrels and runs to the title level, there is reasonable effectiveness of the underlying BM25 rankings, showing that the underlying passage ranking is not unreasonable, and that the hard and interesting problem is in the exact assignment of passages to the “right” headings

Mothers satisfaction of hospital care in the pediatric ward of Kashan Shahid Beheshti hospital during 2010-11

Background: Despite the importance of patient satisfaction as an indicator of care quality, no data are available on the satisfaction of mothers whose children admitted to hospitals. This study aimed to evaluate the mothers satisfaction of hospital care in the pediatric ward of Kashan Shahid Beheshti hospital. Materials and Methods: A descriptive study was conducted using the pediatric family satisfaction (PFS) questionnaire and through interviewing with 280 mothers during 2010-11. Data were analyzed using SPSS software and descriptive statistics. Results: Mean maternal age of subjects was 28.04 years. Average age of admitted children was 2.4 years and the mean of hospital stay 4.97 days. The mothers were dissatisfied with doctors for not being available on time and their failure to notify the results of their childs tests. They were also dissatisfied with nurses for the lack of education about their childs treatment and also the lack of post-discharge care. The mothers were also dissatisfied with welfare services (e.g. providing an appropriate play room for children (71.4%). Average satisfaction scores for the medical, nursing and welfare staff were 22.25±6.19, 29.05±6.88 and 26.68±6.93, respectively. A significant relationship was observed between the child's disease and the mothers satisfaction (P<0.0001). Conclusions: Overall satisfaction with medical, nursing and welfare staff was low in pediatric ward of this hospital. Doctors and nurses attention to the quality of care can reduce the levels of dissatisfaction