The Web has not only grown in size, but also changed its character, due to collaborative content creation and an increasing amount of structure. Current Search Engines find Web pages rather than information or knowledge, and leave it to the searchers to locate the sought information within the Web page. A considerable fraction of Web searches contains named entities. We focus on how the Wikiped...

We describe our participation in the subtopic mining and document ranking subtasks of the NTCIR-9 Intent task, for both Chinese and Japanese languages. In the subtopic mining subtask, we experiment with a novel data-driven approach for ranking reformulations of an ambiguous query. In the document ranking subtask, we deploy our state-ofthe-art xQuAD framework for search result diversification.

In this paper we focus on estimating the post-click engagement on native ads by predicting the dwell time on the corresponding ad landing pages. To infer relationships between feature of the ads and dwell time we resort to the application of survival analysis techniques, which allow us to estimate the distribution of the length of time that the user will spend on the ad. This information is the...

The Spohnian paradigm of ranking functions is in many respects like an order-of-magnitude reverse of subjective probability theory. Unlike probabilities, however, ranking functions are only indirectly—via a pointwise ranking function on the underlying set of possibilities W—defined on a field of propositions A over W . This research note shows under which conditions ranking functions on a field...

Many previous research studies on extractive text summarization consider a subset of words in a document as keywords and use a sentence ranking function that ranks sentences based on their similarities with the list of extracted keywords. But the use of key concepts in automatic text summarization task has received less attention in literature on summarization. The proposed work uses key concep...

Distributions over rankings are used to model data in various settings such as preference analysis and political elections. The factorial size of the space of rankings, however, typically forces one to make structural assumptions, such as smoothness, sparsity, or probabilistic independence about these underlying distributions. We approach the modeling problem from the computational principle th...

We present a feature-rich discriminative model for machine translation which uses an abstract semantic representation on the source side. We include our model as an additional feature in a phrase-based decoder and we show modest gains in BLEU score in an n-best re-ranking experiment.