This paper studies a new query on uncertain data, called k-selection query. Given an uncertain dataset of N objects, where each object is associated with a ranking score and a presence probability, a k-selection query returns k objects such that the expected ranking score of the best available objects is maximized. This query is useful in several applications such as information retrieval and d...

This paper describes a novel method of improving machine transliteration by using multiple transliteration hypotheses and re-ranking them. We constructed seven machine-transliteration engines to produce a set of transliteration hypotheses. We then re-ranked the hypotheses to select the correct transliteration hypothesis. We propose a re-ranking method that makes use of confidence-score, languag...

Briefly, the algorithm starts by specifying no constraints (step 1 of Algorithm 1: W is initialized to the null set). At each iteration, it adds a single constraint, which corresponds to the most violated ranking (step 4 of Algorithm 1: solving problem (1)). Intuitively, problem (1) finds a ranking that differs significantly from the ground-truth ranking in terms of AP but has a high score. Hav...

We consider a setting where a system learns to rank a fixed set of m items. The goal is produce good item rankings for users with diverse interests who interact online with the system for T rounds. We consider a novel top-1 feedback model: at the end of each round, the relevance score for only the top ranked object is revealed. However, the performance of the system is judged on the entire rank...

In this paper, we present a new string pattern matching-based passage ranking algorithm for extending traditional textbased QA toward videoQA. Users interact with our videoQA system through natural language questions, while our system returns passage fragments with corresponding video clips as answers. We collect 75.6 hours videos and 253 Chinese questions for evaluation. The experimental resul...

We propose an approach for ranking microblog search results. The basic idea is to leverage user engagement for the purpose of ranking: if a microblog post received many retweets/replies, this means users find it important and it should be ranked higher. However, simply applying the raw count of engagement may bias the ranking by favoring posts from celebrity users whose posts generally receive ...

Social media are used by an increasing number of political actors. A small subset of these is interested in pursuing extremist motives such as mobilization, recruiting or radicalization activities. In order to counteract these trends, online providers and state institutions reinforce their monitoring efforts, mostly relying on manual workflows. We propose a machine learning approach to support ...

In current Semantic Web community, some researches have been done on ranking ontologies, while very little is paid to ranking vocabularies within ontology. However, finding important vocabularies within a given ontology will bring benefits to ontology indexing, ontology understanding and even ranking vocabularies from a global view. In this paper, Vocabulary Dependency Graph (VDG) is proposed t...

The ranking and prediction of novel therapeutic categories for existing drugs (drug repositioning) is a challenging computational problem involving the analysis of complex chemical and biological networks. In this context we propose a novel semi-supervised learning problem: ranking drugs in integrated bio-chemical networks according to specific DrugBank therapeutic categories. To deal with this...

This paper discusses the implementation of Support Vector Machine (SVM) based ranking metrics to compute the similarity and rank the documents based on a user query using Fuzzy Logic. TREC dataset has been used for the same purpose. The dataset is parsed to generate keywords index which is used for the similarity comparison with the user query and ranking is done using SVM. Each query is assign...