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...

Given a set V of n objects, an online ranking system outputs at each time step a full ranking of the set, observes a feedback of some form and suffers a loss. We study the setting in which the (adversarial) feedback is an element in V , and the loss is the position (0th, 1st, 2nd...) of the item in the outputted ranking. More generally, we study a setting in which the feedback is a subset U of ...

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...

After the phenomenal success of the PageRank algorithm, many researchers have extended the PageRank approach to ranking graphs with richer structures beside the simple linkage structure. In some scenarios we have to deal with multi-parameters data where each node has additional features and there are relationships between such features. This paper stems from the need of a systematic approach wh...

Learning To Rank (LTR) techniques aim to learn an effective document ranking function by combining several document features. While the function learned may be uniformly applied to all queries, many studies have shown that different ranking functions favour different queries, and the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each indi...

We propose a set of solutions for managing a large scale RDF semantic repository from the perspective of RDF graph model. A native storage instead of relational database is used to hold RDF. Indices supporting regular path expression, full-text retrieval and partial OWL Lite inference are built above the storage model. Semantic ranking for resources are provided as well.

We present a deep convolutional neural network approach for producing semantic segmentations. First, we generalize the architecture of the successful Alexnet network [7] to directly predict coarse segmentations. Second, we produce full resolution segmentations by re-ranking a diverse set of plausible segmentation proposals generated from a recent state of the art approach [9].

In the past few years, there has been much work on incorporating fairness requirements into algorithmic rankers, with contributions coming from data management, algorithms, information retrieval, and recommender systems communities. this survey, we give a systematic overview of work, offering broad perspective that connects formalizations approaches across sub-fields. An important contribution ...