Specifically, we deal with a ranking problem with a collection of financial reports, in which each report is associated with a company. By using text information in the reports, which is so-called the soft information, we apply learning-to-rank techniques to rank a set of companies to keep them in line with their relative risk levels. In our experiments, a collection of financial reports, which...

Newspaper websites and news aggregators rank news stories by their newsworthiness in real-time for display to the user. Recent work has shown that news stories can be ranked automatically in a retrospective manner based upon related discussion within the blogosphere. However, it is as yet undetermined whether blogs are sufficiently fresh to rank stories in real-time. In this paper, we propose a...

Received for publication 11 February 1980 and in revised form 24 March 1980. of their healthy HLA-identical siblings. These data indicate that the impairment of autologous MLR in some patients is due to a reduction or dysfunction of responder T cell activity and not to a defect of autologous stimulator cells.

For massive and heterogeneous modern data sets, it is of fundamental interest to provide guarantees on the accuracy of estimation when computational resources are limited. In the application of learning to rank, we provide a hierarchy of rankbreaking mechanisms ordered by the complexity in thus generated sketch of the data. This allows the number of data points collected to be gracefully traded...

Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has ...

The 2017 CLEF eHeath Task2 requires to rank the retrieval results given by medical database. The purpose is to reduce efforts that experts devote to finding indeed relevant documents. We utilize a customized Learning-to-Rank model to re-rank the retrieval result. Additionally, we adopt word2vec to represent queries and documents and compute the relevant score by cosine distance. We find that th...

We describe the submissions of ILLC UvA to the metrics and tuning tasks on WMT15. Both submissions are based on the BEER evaluation metric originally presented on WMT14 (Stanojević and Sima’an, 2014a). The main changes introduced this year are: (i) extending the learning-to-rank trained sentence level metric to the corpus level (but still decomposable to sentence level), (ii) incorporating synt...

This paper describes our participation in the Federated Web Search track at TREC 2014. For the resource selection task we employ a learning-to-rank approach to combine various (instantiations of) resource ranking models. For the vertical selection task we treat the estimated collection relevance scores as binary judgements.

LambdaMART is the boosted tree version of LambdaRank, which is based on RankNet. RankNet, LambdaRank, and LambdaMART have proven to be very successful algorithms for solving real world ranking problems: for example an ensemble of LambdaMART rankers won Track 1 of the 2010 Yahoo! Learning To Rank Challenge. The details of these algorithms are spread across several papers and reports, and so here...