Assume a teacher provides examples one by one. An approximate incremental SVM computes a sequence of classi ers that are close to the true SVM solutions computed on the successive incremental training sets. We show that simple algorithms can satisfy an averaged accuracy criterion with a computational cost that scales as well as the best SVM algorithms with the number of examples. Finally, we ex...

This paper presents a study of employing Ranking SVM and Convolutional Neural Network for two missions: legal information retrieval and question answering in the Competition on Legal Information Extraction/Entailment. For the first task, our proposed model used a triple of features (LSI, Manhattan, Jaccard), and is based on paragraph level instead of article level as in previous studies. In fac...

This paper describes the ITNLP system participated in the Knowledge Base Population (KBP) track English Entity Linking task. Our Entity linking system is composed of three parts: candidate generation, candidate ranking and nil clustering. In the candidate generation process, the redirect pages and anchor texts in Wikipedia are utilized to generate candidate entities for the mentions. Ranking SV...

Outlier detection is an important task in data mining because outliers can be either useful knowledge or noise. Many statistical methods have been applied to detect outliers, but they usually assume a given distribution of data and it is difficult to deal with high dimensional data. The Statistical Learning Theory (SLT) established by Vapnik et aI. provides a new way to overcome these drawbacks...

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...