The NTCIR-9 GeoTime task is to retrieve documents to answer such questions as when and where certain events happened. In this paper we propose a Passage-Based Learning to Rank (PGLR) method to address this task. The proposed method recognizes texts both strongly related to the target topics and containing geographic and temporal expressions. The implemented system provides more accurate search ...

In TREC 2014, we focus on tackling the challenges posed by the Contextual Suggestion and Temporal Summarisation tracks, as well as enhancing our existing technologies to tackle risk-sensitivity as part of the Web track, building upon our Terrier Information Retrieval Platform. In particular, for the Contextual Suggestion track, we propose a novel bundled venue retrieval approach and experiment ...

We present a new learning to rank framework for estimating context-sensitive term weights without use of feedback. Specifically, knowledge of effective term weights on past queries is used to estimate term weights for new queries. This generalization is achieved by introducing secondary features correlated with term weights and applying regression to predict term weights given features. To impr...

This paper proposes a method to enhance person re-identification by integrating gait biometric. The framework consists of the hierarchical feature extraction and matching methods. Considering the appearance feature is not discriminative in some cases, the feature in this work composes of the appearance feature and the gait feature for shape and temporal information. In order to solve the view-a...

—The International Organización for Standardization has decided to develop a new metadata standard for learning resources. This standard claims to enhance the compatibility, flexibility and focus on the user requirements. This article presents the new ISO/IEC MLR standard, describes it and compares the MLR data elements to those defined in Dublin Core and in LOM.

Abstract Learning-to-rank techniques have proven to be extremely useful for prioritization problems, where we rank items in order of their estimated probabilities, and dedicate our limited resources to the top-ranked items. This work exposes a serious problem with the state of learning-to-rank algorithms, which is that they are based on convex proxies that lead to poor approximations. We then d...

We are interested in supervised ranking with the following twist: our goal is to design algorithms that perform especially well near the top of the ranked list, and are only required to perform sufficiently well on the rest of the list. Towards this goal, we provide a general form of convex objective that gives high-scoring examples more importance. This “push” near the top of the list can be c...

In this paper, we compare the effectiveness of basic acoustic features and genre annotations when adapting a music similarity model to user ratings. We use the Metric Learning to Rank algorithm to learn a Mahalanobis metric from comparative similarity ratings in in the MagnaTagATune database. Using common formats for feature data, our approach can easily be transferred to other existing databas...

Under the background of big data era today, once been widely used method – multiple linear regressions can not satisfy people’s need to handle big data any more because of its bad characteristics such as multicollinearity, instability, subjectivity in model chosen etc. Contrary to MLR, LASSO method has many good natures. it is stable and can handle multicollinearity and successfully select the ...

[1] R-.C. Chen, D. Spina, W.B. Croft, M. Sanderson, and F. Scholer. Harnessing Semantics for Answer Sentence Retrieval. In Proceedings of ESAIR'15, 2015 [5] D. Metzler and T. Kanungo. Machine Learned Sentence Selection Strategies for QueryBiased Summarization. In Proceedings of SIGIR 2008 Learning to Rank Workshop, 2008 [7] L. Yang, Q. Ai, D. Spina, R.-C. Chen, L. Pang, W.B. Croft, J. Guo, and ...