Query variation performance prediction for systematic reviews

When conducting systematic reviews, medical researchers heavily deliberate over the final query to pose to the information retrieval system. Given the possible query variations that they could construct, selecting the best performing query is difficult. This motivates a new type of query performance prediction (QPP) task where the challenge is to estimate the performance of a set of query variations given a particular topic. Query variations are the reductions, expansions and modifications of a given seed query under the hypothesis that there exists some variations (either generated from permutations or hand crafted) which will improve retrieval effectiveness over the original query. We use the CLEF 2017 TAR Collection, to evaluate sixteen pre and post retrieval predictors for the task of Query Variation Performance Prediction (QVPP). Our findings show the IDF based QPPs exhibits the strongest correlations with performance. However, when using QPPs to select the best query, little improvement over the original query can be obtained, despite the fact that there are query variations which perform significantly better. Our findings highlight the difficulty in identifying effective queries within the context of this new task, and motivates further research to develop more accurate methods to help systematic review researchers in the query selection process

Machine Learning, Quantum Mechanics, and Chemical Compound Space

We review recent studies dealing with the generation of machine learning models of molecular and solid properties. The models are trained and validated using standard quantum chemistry results obtained for organic molecules and materials selected from chemical space at random

Fertility and its Meaning: Evidence from Search Behavior

Fertility choices are linked to the different preferences and constraints of individuals and couples, and vary importantly by socio-economic status, as well by cultural and institutional context. The meaning of childbearing and child-rearing, therefore, differs between individuals and across groups. In this paper, we combine data from Google Correlate and Google Trends for the U.S. with ground truth data from the American Community Survey to derive new insights into fertility and its meaning. First, we show that Google Correlate can be used to illustrate socio-economic differences on the circumstances around pregnancy and birth: e.g., searches for "flying while pregnant" are linked to high income fertility, and "paternity test" are linked to non-marital fertility. Second, we combine several search queries to build predictive models of regional variation in fertility, explaining about 75% of the variance. Third, we explore if aggregated web search data can also be used to model fertility trends.Comment: This is a preprint of a short paper accepted at ICWSM'17. Please cite that version instea