Most existing search engines employ static ranking algorithms that do not adapt to the specific needs of users. Recently, some researchers have studied the use of clickthrough data to adapt a search engine’s ranking function. Clickthrough data indicate for each query the results that are clicked by users. As a kind of implicit relevance feedback information, clickthrough data can easily be coll...

Despite the enormous commercial importance of on-line advertisements (ads), there has been little work done to clarify the basis for ranking and displaying them. Most existing methods rank ads as if the user views each of them in isolation. We will consider a more realistic user model that induces three mutual influences between displayed ads: (i) positional bias (for viewing ads placed higher ...

This article explores a combination of deep and shallow approaches to the problem of resolving the scope of speculation and negation within a sentence, specifically in the domain of biomedical research literature. The first part of the article focuses on speculation. After first showing how speculation cues can be accurately identified using a very simple classifier informed only by local lexic...

We discuss how the scope of speculation and negation can be resolved by learning a ranking function that operates over syntactic constituent subtrees. An important assumption of this method is that scope aligns with constituents, and hence we investigate instances of disalignment. We also show how the method can be combined with an existing scope-resolution system based on manually-crafted rule...

We consider the problem of learning a ranking function, that is a mapping from instances to rankings over a finite number of labels. Our learning method, referred to as ranking by pairwise comparison (RPC), first induces pairwise order relations from suitable training data, using a natural extension of so-called pairwise classification. A ranking is then derived from a set of such relations by ...

The discovery of invariants and ranking functions plays a central role in program verification. In our previous work, we investigated invariant generation and non-linear ranking function discovering of polynomial programs by reduction to semi-algebraic systems solving. In this paper we will first summarize our results on the two topics and then show how to generalize the approach to discovering...

A new link-based document ranking framework is devised with at its heart, a contents and time sensitive random literature explorer designed to more accurately model the behaviour of readers of scientific documents. In particular, our ranking framework dynamically adjusts its random walk parameters according to both contents and age of encountered documents, thus incorporating the diversity of t...

Using several simplifications of the vector-space model for text retrieval queries, the authors seek the optimal balance between processing efficiency and retrieval effectiveness as expressed in relevant document rankings. fficient and effective text retrieval techniques are critical in managing the increasing amount of textual information available in electronic form. Yet text retrieval is a d...

In this paper, we present Farasa, a fast and accurate Arabic segmenter. Our approach is based on SVM-rank using linear kernels. We measure the performance of the segmenter in terms of accuracy and efficiency, in two NLP tasks, namely Machine Translation (MT) and Information Retrieval (IR). Farasa outperforms or is at par with the stateof-the-art Arabic segmenters (Stanford and MADAMIRA), while ...

In this paper, we explore the use of new page segmentation algorithm using both visual and structural mark-up (,) to partition web pages into blocks and investigate how to take advantage of block-level evidence to improve retrieval performance in the web. We propose a new ranking function that combines content and link rank based on propagation of scores over links on block-to-page grap...