The previous work has justified the assumption that document ranking can be improved by further considering the coarse-grained relations in various linguistic levels (e.g., lexical, syntactical and semantic). To the best of our knowledge, little work is reported to incorporate the fine-grained ontological relations (e.g., ) in document ranking. Two contributions are wo...

In this paper, we present a general learning framework which treats the ranking problem for various Information Retrieval tasks. We extend the training set generalization error bound proposed by [4] to the ranking case and show that the use of unlabeled data can be beneficial for learning a ranking function. We finally discuss open issues regarding the use of the unlabeled data during training ...

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...

With the growth of different search engines, it becomes difficult for an user to search particular information effectively. If a search engine could provide domain specific information such as that confines only to a particular topicality, it is referred to as domain specific engine. Applying the ranking model trained for broad-based search to a domain specific search does not achieve good perf...

Route instructions for pedestrians are usually better understood if they include references to landmarks, andmoreover, these landmarks should be as salient as possible. In this paper, we present an approach for automatically deriving a mathematical model of salience directly from route instructions given by humans. Each possible landmark that a person can refer to in a given situation is modell...

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 ...