Text readability is typically defined in terms of “grade level”; the expected educational level of the reader at which the text is directed. Mechanisms for measuring readability in English documents are well established; however this is not in case in many other languages, such as syllabic alphabetic languages. In this paper seven different mechanisms for assessing the readability of syllabic a...

Aggregate ranking tasks are those where documents are not the final ranking outcome, but instead an intermediary component. For instance, in expert search, a ranking of candidate persons with relevant expertise to a query is generated after consideration of a document ranking. Many models exist for aggregate ranking tasks, however obtaining an effective and robust setting for different aggregat...

This paper provides an overview of the NTCIR-10 INTENT-2 task (the second INTENT task), which comprises the Subtopic Mining and the Document Ranking subtasks. INTENT-2 attracted participating teams from China, France, Japan and South Korea – 12 teams for Subtopic Mining and 4 teams for Document Ranking (including an organisers’ team). The Subtopic Mining subtask received 34 English runs, 23 Chi...

In many ranking tasks in machine learning, the goal is to construct a scoring function f:X → R, where X⊂R, that can be used to rank a set of labeled examples {(x�,y�)}��� � , where x� ∈ X and y� ∈ {0,1}, that are randomly drawn from an unknown distribution on X × {0,1}. We present a mixed integer programming (MIP) method to generate this scoring function. In particular, the scoring function is ...

Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be ...

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to ...

Bipartite ranking aims to learn a real-valued ranking function that orders positive instances before negative instances. Recent efforts of bipartite ranking are focused on optimizing ranking accuracy at the top of the ranked list. Most existing approaches are either to optimize task specific metrics or to extend the ranking loss by emphasizing more on the error associated with the top ranked in...