Due to the ability of knowledge graph effectively solve sparsity problem collaborative filtering, (KG) has been widely studied and applied as auxiliary information in field recommendation systems. However, existing KG-based methods mainly focus on learning its representation from neighborhood target items, ignoring influence other items item. The focuses local feature item, which is not suffici...

In this paper, we evaluate a set of methods for combining features for cover song identification. We first create multiple classifiers based on global tempo, duration, loudness, beats and chroma average features, training a random forest for each feature. Subsequently, we evaluate standard combination rules for merging these single classifiers into a composite classifier based on global feature...

In this paper we present a maximum entropy Word Sense Disambiguation system we developed which performs competitively on SENSEVAL-2 test data for English verbs. We demonstrate that using richer linguistic contextual features significantly improves tagging accuracy, and compare the system’s performance with human annotator performance in light of both fine-grained and coarse-grained sense distin...

Extracting and fusing discriminative features in fingerprint matching, especially in distorted fingerprint matching, is a challenging task. In this paper, we introduce two novel features to deal with nonlinear distortion in fingerprints. One is finger placement direction which is extracted from fingerprint foreground and the other is ridge compatibility which is determined by the singular value...

Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations, ...) within a document into predefined categories. Correctly identifying these phrases plays significant role in simplifying access. However, it remains difficult task because (NEs) have multiple forms they are context dependent. While the can be...