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Patents contain important technical, legal and economic information. Their annual publication accounts for a quarter of the books and journals in the world. Along with the growing number of patents, patent clustering analysis is becoming more and more important. We focus on two key issues in patent clustering, namely patent representation and data visualization. Firstly, patents are represented as...
Document representation as Generalized Latent Semantic Analysis (GLSA) vectors were claimed to give performance improvement on several task such as synonymy test, document classification, and clustering compared to traditional Latent Semantic Analysis (LSA) based systems, however GLSA performance has never been tested on automated essay grading system. This experiment propose an GLSA based automatic...
In this paper, we examine the significance of expansion of the user query by two techniques namely Efficient Clustering-By-Direction and Theme Clustering. These two techniques produce the clusters of keywords extracted from the set of retrieved documents for the user query. The former clustering is based on statistical approach whereas the latter clustering is based on semantic approach. Empirical...
As the sharable and reusable domain knowledge, domain ontology increasingly serves as a foundation for semantic Web. Personalized management of domain ontologies is to provide personalized views of domain ontologies to users during run time according to user preferences. It helps that users can focus on their interested parts, instead of the whole. It increases the efficiency of ontology-based application...
With music markets shifting, the use of music in video productions has become increasingly important. Our novel research project “SyncGlobal“ addresses this global music licensing opportunity. Our goal is to find the best acoustic or semantic matches to any video sequence from large-scale intercultural music catalogs with minimal human effort. One important aspect is the retrieval of music excerpts...
Hashing methods have attracted much attention in large scale image research in recent years, because they are not only fast, but also needing a little memory. This paper proposed a balanced semi-supervised hashing method by dividing image into several blocks. With the help of improved semi-supervised hashing, we obtain a short hash code of each block, which jointed together forms a hash code of an...
A key problem in image annotation is to learn the underlying semantics. However, finding such semantic embeddings is a challenge task and often requires large amount of tagging information. In this paper, we propose to utilize multi-modality cues by incorporating visual and textual information as embedded objects. The paper further presents a multi-task learning framework that simultaneously learns...
Recently Non-negative Matrix Factorization (NMF) has captured a quantifiable attention in the field of information retrieval. Especially in the dimension of text summarization, it has leaped a large step in minimizing the gap between the summary produced by the man and machine. This paper is aimed at a preliminary study of a novel approach to automatic text summarization using extraction method by...
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